Review Irecos

Energy Efficient Routing Protocols for Wireless Sensor Networks:

A Survey

K S Shivaprakasha, Muralidhar Kulkarni

Abstract: Wireless Sensor Networks (WSNs) have become one of the emerging trends of the modern communication

systems. They find their applications in various fields like habitat monitoring, home automation, environment monitoring,

battle filed environment etc. WSNs are different from Mobile Adhoc Networks in the perspective of energy awareness,

adaptive communication patterns and the routing algorithms. As the sensor devices are powered by batteries, which cannot

be recharged often, the power awareness is one of the major requirements in WSNs. Many energy aware routing protocols

have been proposed in the literature. In this survey, an attempt has been made to summarize the various energy aware

routing protocols available in the literature and also a comparative analysis of these has been made considering various

network parameters like the delay, routing overhead, QoS, type of routing protocol etc.

Key Words: Wireless Sensor Networks (WSN), Base Station (BS), Cluster Head (CH), Medium Access Control (MAC), Region Head (RH)

I. Introduction

A Wireless Sensor Network (WSN) is a network

of hundreds of small devices called sensors, which are

deployed either randomly or uniformly over a geographical

area. Each node is capable of sensing a physical entity like

temperature, pressure, humidity etc [1, 2]. The sensed

information will be conveyed to the base station. There are

three ways of communicating the information to the Base

Station (BS): by direct communication, via intermediate

nodes or using clustering method. The first method is

feasible only if the BS is in the close proximity of the

sender node. Thus multi hop transmission is used in which the sender has to rely on the intermediate nodes to reach

the BS. Alternatively the nodes can also be grouped into

clusters with one node being the Cluster Head (CH) and the

communication to the base station will always be via CH.

Generally sensor nodes consist of a Sensor,

Processing unit, transmitter, position finding system and

power units. Power units cannot be recharged often and

thus the data transmission has to be done with the

minimum energy consumption.

Routing in wireless sensor networks is different

than the IP based routing algorithms, as global addressing

schemes cannot be used here [3]. Also in WSNs data gathered is more important than the information about the

node that has the data. Thus the routing protocol has to be

content based. Also as the number of nodes will be large, it

is more likely to happen that more than one node can have

the same data. Thus the data aggregation also has to be

done in order avoid the redundancy. Finally as the nodes

are equipped with the limited power resource, the routing

protocol has to be energy aware.

Routing in WSNs has been categorized into

various categories. They can be classified as proactive,

reactive and hybrid protocols. Proactive protocols are

generally called as table driven protocols in which the

route to the base station from every node will be

determined a priori [4, 5]. These algorithms can be used for

less dynamic networks. Whereas in reactive protocols the

path is discovered only when it is required thus reducing a

lot of routing overhead as compared to proactive protocols.

Thus they are also called as on-demand protocols. Finally in Hybrid protocols the features of both proactive and

reactive algorithms have been included [6].

Another way of classification is based on the

network structure as either a flat or hierarchical routing. In

flat routing each of the node acts independently whereas in

hierarchical routing, nodes are grouped into clusters with a

CH node and all transmissions will be via the CH. Routing

protocols can also be categorized as either time driven,

event driven or query based [8, 9]. In time driven

protocols, nodes will be in active state for a fixed amount

of time in a periodical manner and senses the data. In case of event driven, the nodes sense the data only when a

considerable change in the entity has occurred. And in case

of query based, the base station will send a request for the

data when it is required and the nodes will reply to the

request [10, 11].

A lot of work has been carried out in the field of

routing in WSNs [12, 13, 14]. In this paper a survey has

been made on the energy aware routing protocols proposed

in the literature. Also a qualitative comparison of the same

has been made over various network parameters like

routing overhead, delay, QoS, data aggregation etc.

The rest of the paper is organized as follows: Section II deals with the various design issues to be considered for

the routing protocol in WSN. Section III summarizes

various energy aware routing protocols proposed for

WSNs. In section IV a comparative analysis of the

protocols has been made. Finally section V gives the

concluding remarks of the paper.

II. Routing Challenges in Wireless Sensor

Networks

As discussed in Section I, routing in WSN is a

challenging task. Wireless medium, limited resource

availability, hostile environment pose many restrictions on

the routing protocols in WSNs. In this section a brief notes

on the design challenges for a WSN is studied [15, 16].

II.1 Energy Usage

As the available energy in the nodes of a WSN is limited, the proposed routing protocol has to optimally use

the available resources. If a node’s battery gets extinct, the

node becomes dead which may lead to network

partitioning. Thus the energy awareness is directly related

to the survivability of the network [17, 18].

II.2 Data Aggregation

As the nodes may generate redundant data, the

transmission of the same will increase the network traffic,

which in turn decreases the throughput. Thus the

combining of the data has to be performed which is called

as data aggregation [19, 20]. This can be done using various methods like duplicate suppression etc. Data

aggregation will results in an efficient routing consuming

less energy.

II.3 Mobility of the Nodes

Although WSNs are assumed to be stationary for

most of the cases, there are some applications where the

mobility of the nodes also has to be considered. Thus the

routing protocols proposed have to be dynamic to

accommodate the changes in the network. Also the event

can be either static or dynamic. For example it is dynamic

for tracking application whereas it is static in case of monitoring systems [21].

II.4 Data Transmission Model

One of the major issues to be considered in

routing in WSNs is when to send the sensed data to the BS.

There are various models proposed for the same viz time

driven model, event driven model and the query-based

model. As mentioned in Section I, in case of the time

driven model, the node will be active for a certain period of

time and senses the data. Whereas in case of Event driven,

the node will awake only when there is a significant change

in the sensing entity. Finally in query-based model, the BS

has to initiate the process by broadcasting request signal to the nodes [22].

II.5 Deployment of Nodes

Node deployment also has an effect on the

working of the routing protocol. The deployment of the

nodes is dependent on the type of the application. It can be

either random or deterministic. In case of random

deployment nodes will be scattered over a geographical

area in a random fashion, which may lead to the formation

of the network in an adhoc manner. Whereas in case of deterministic approach, the nodes are manually placed in

some order in which the routing algorithms can be simpler

[23].

II.6 Scalability

As mentioned earlier, the wireless sensor networks

consist of hundreds of or even thousands of nodes. Also the

nodes can join or leave the network with time. Thus it is

desirable to have the routing protocol, which should be

capable of accommodating the new nodes without affecting

the behavior of the network.

III. Energy Aware Routing Protocols

As the energy conservation is a vital issue in the

performance of the WSN, many protocols have been

proposed considering Energy Awareness. In this section we

will be summarizing some of the protocols proposed in the

literature.

Low-Energy Adaptive Clustering Hierarchy (LEACH)

LEACH is a novel cluster based routing protocol proposed

in [24]. In this protocol the CHs are chosen on the rotation

basis so that the load distribution amongst nodes is almost

uniform. Initially each of the nodes has to decide whether to become the CH in the present round depending on some

probability. Cluster members are decided based on the

distance from the CHs.

Geographical Adaptive Fidelity (GAF)

GAF is an energy conservative routing protocol, which is

independent of the underlying routing protocol [25]. It

conserves energy by identifying and turning off the

unnecessary nodes in the network. In GAF the whole

network is divided into small grids. The algorithm operates

in three phases: Discovery, Active and Sleep.

Geographic and Energy Aware Routing (GEAR)

GEAR algorithm is an energy aware algorithm, which

selects its neighbor based on the energy parameter and

geographical information [26]. In GEAR the next hop is

selected as a node N if it is closer to the destination with

respect to the learned parameter. Learned parameter

includes the energy and the distance parameter.

Threshold sensitive Energy Efficient sensor Network

protocol (TEEN)

Time constraint cannot be relaxed for some critical

applications in WSNs. TEEN is a reactive protocol, which

manages time sensitive applications [27]. It is a cluster

based routing where CH does the data aggregation. By

managing the thresholds time criticality can be ensured to

time sensitive applications.

Energy Efficient Routing (EER)

Traditional protocols concentrate only on the shortest path

but do not take into account the available battery power at

each node. In Gradient-Based Routing (GBR), while being

flooded, the ‘interest’ message records the number of hops

taken. This allows a node to discover the minimum number

of hops to the user, called the node’s height. When a node

detects that its energy reserve has dropped below a certain

threshold, it discourages others from sending data to it by

increasing its height [28].

Power-Efficient Gathering in Sensor Information

Systems (PEGASIS)

PEGASIS is a cluster based energy aware routing

algorithm and is a near optimal chain based routing [29].

Each node communicates only with its neighbor but not to

the CH directly, which reduces the energy consumption at

each node. Chain formation will start from the farthest

node to the BS. Chain can be reconstructed whenever a

node dies in the chain.

Energy Band based Routing Protocol for Wireless Sensor

Networks (EBRP)

EBRP is a stateless routing protocol in which the network

is divided into various energy bands and the routing is done

based on the energy bands [30]. It focuses on the optimal

usage of available energies in all nodes. A virtual tree is

formed based on the residual energies of nodes in the

network with the nodes of lowest energies forming the

leaves. Nodes with the same energy level and in each

other’s vicity form the nodes at the same level. In each

level, nodes will communicate only with the higher-level

nodes. Thus nodes with less energy will not be burdened.

Energy-aware Routing to a Mobile Gateway (EARMG)

Most of the energy aware protocols proposed consider the

BS to be stationary which may not be true for all

applications. Thus some modifications over the existing

protocols have to be suggested so as to consider the

mobility of the BS. EARMG is one such algorithm [31].

Location of the gateway has to be intimated to other nodes

so as to re discover routes.

Hybrid Energy-Efficient Distributed clustering (HEED) HEED is a dynamic clustering algorithm where the CHs

are changed periodically based on the residual energy and

the degree of the nodes [32]. Nodes are considered to be

quasi stationary. Initially the CHs are formed based on

their residual energy. Number of clusters is predetermined.

Nodes will then join appropriate clusters so as to minimize

the transmission cost.

Gossip Based Routing (GBR)

GBR is an energy efficient protocol, which reduces the flooding traffic [33]. Each node floods the message with

some probability. Also a lot of energy can be saved if the

nodes enter sleep mode whenever they are not active. In

GSP, at the beginning of each period, node will decide

whether to enter sleep mode or not with some probability

p. Nodes awake at the end of each period and the same

process is repeated.

Energy-Aware Routing Protocol (EARP)

In the traditional routing algorithms like AODV, the path

between the source and the destination will be erased after a certain amount of time, which may lead to frequent route

discovery initiations. Whereas in case of EARP the table

retains all the paths that are less likely to be expired. Thus

the same path can be used, if the communication between

the same set of nodes have to be established [34].

Energy Aware Random Asynchronous Wakeup (RAWE)

Nodes which are capable of acting i.e to perform a

particular job are called as actor nodes eg: Robot. Actor

nodes are provided with more battery backup than the

sensor nodes. RAW-E distributes the load among the nodes

in the forwarding set in proportional to their remaining energy. RAW –E prefers to use actor node [35].

COordination-based Data dissemination for sEnsor

networks (CODE)

CODE addresses the situations where the BS is mobile and

the sensors are stationary [36]. It is assumed that all nodes

know their geographic information. CODE involves three

phases: data announcement, query transfer and data

dissemination.

SInk cluster – based data Dissemination for sEnsor

networks (SIDE)

SIDE is defined for large number of stationary sink nodes

[36]. When a set of nodes in the target region receives a

query message, one of them will be chosen as a source and

it does the data fusion. If a data has to be sent to multiple

sinks, instead of sending the data to all sinks via separate

paths, source sends the data to one of the sinks and the sink

nodes will then share the information amongst themselves.

Improved Weighting Clustering Algorithm (IWCA)

IWCA is a cluster-based algorithm in which a node with higher degree is selected as the CH [37]. It is more

applicable for mobile nodes. A node with less mobility is

chosen as a CH. CH selection is based on its residual

energy, mobility and its distance to its neighbors

Genetic Annealing based Clustering Algorithm (GACA)

GACA is hierarchical routing protocol in which the CHs

are selected on the iterative basis [37]. Genetic Algorithm

(GA) or Simulated Annealing (SA) is considered to select

the optimal CHs.

Base-Station Controlled Dynamic Clustering Protocol

(BCDCP)

In [38] authors have proposed a centralized cluster based

routing. Initially BS receives the energy levels of all nodes

and computes a set of nodes with energy more than the

average energy. Nodes from that set will be chosen as CHs.

Cluster formation is done in iterative manner starting with

two clusters in the network. Nodes will be allocated to

respective clusters depending on the distance. BS will then

computes the minimum paths using spanning tree approach and will be intimated to the nodes.

Cluster based Energy Efficient Routing Protocol

(CBEERP)

CBEERP is a cluster based routing protocol without

considering the location information of the nodes. The

algorithm involves two phases: cluster construction phase

and data transmission phase. Initially BS broadcasts an

advertisement message for CH selection. Once CHs are

chosen, they advertise to other nodes and all nodes will

join appropriate clusters [39, 24].

Optimal Energy-Efficient Routing (OEER)

It is a table driven routing protocol in which the Bellman

Ford Algorithm is incorporated for routing. OEER balances

the minimum and average node lifetime [40]. Routing

problem is formulated as a non-linear optimization

problem. Langrangean Relaxation is being applied to solve

the problem

Energy Efficient AODV (EEAODV)

An improvement over AODV has been proposed in which

the residual energy of the intermediate nodes will be considered [41]. This has been accomplished by

introducing an additional field in the RREQ packet,

Minimum Residual Energy (Min-RE). Each of the

intermediate nodes updates the Min- RE field. After

destination receiving the RREQ messages, the ratio of Min

RE and hop count is calculated and the path with largest

ratio is chosen.

Energy Aware Distance Vector Routing Protocol (EADV)

In EADV, initially the sink node broadcasts Initial

Broadcast (IB) packet to its neighbors. Each of the nodes receiving the IB packet, extracts the table information and

stores in it and updates the fields like hop count and cost

and forwards further [42]. The IB packets will be

forwarded in the network repeatedly till all nodes update its

Hop count properly. Depending on the remaining energy in

the node, the cost factor can be either linear, quadratic or

cubic.

Minimum Transmission Energy with Clustering

Hierarchy (MTECH) MTECH is a hierarchical routing protocol that uses cluster

model. Node in the cluster having the highest energy will

be chosen as a CH [43].

Energy Efficient Clustering Routing Algorithm (EECR)

One of the ways to incorporate the energy efficiency is to

design a protocol so as to distribute the load uniformly

amongst nodes. EECR is a one such cluster based

algorithm [44]. BS does cluster formation and the selection

of CHs. Algorithm is divided into two phases cluster

formation and data transmission. BS does the job of cluster formation by iteratively dividing the network into sub

networks.

Reliable Energy Aware Routing (REAR)

REAR is an on demand routing protocol in which the

energy extinction is reduced by avoiding retransmission of

the packets [45]. Algorithm works in four phases: Path

discovery, energy reservation, reliable transmission and

reserved resource release. When a node receives an interest

message, it checks for a path to the BS. If path does not

exists, service path discovery is initiated. In the meanwhile

BS will discover a backup path to the sender. Energy is reserved in the path depending on the requirement.

Energy Efficient Clustering Algorithm (EECA)

EECCA is a centralized clustering algorithm in which the

whole network topology will be notified to the BS using

notification algorithm. The BS then will decide the

clusters. Notification protocol involves two phases:

ascending phase from nodes to the BS telling their

existence and the descending phase from the BS to the

nodes informing the cluster to which it belongs [46].

Distributed Energy-Efficient Clustering Algorithm

(DEEC)

DEEC is a distributed algorithm for heterogeneous network

[47]. The CHs are elected based on the ratio of the residual

energy and the average energy in the network. Nodes with

more initial and residual energy have more chances of

becoming the CH.

Energy-Efficient Clustering Algorithm (ECA)

Energy efficient Clustering Algorithm (ECA) is a dynamic

clustering algorithm [48]. Algorithm involves two phases viz setup phase and steady state phase. Time stamp and

TTL (Time to Live) is used in the message to adjust the

diameter of the cluster.

Distance-Based Proportional Delay Differentiation

(DPDD)

Most of the energy efficient routing algorithms do not offer

a good QoS which are not suitable for real time

applications. DPDD is a QoS assured energy efficient routing in which end to end delay requirement is satisfied

[49]. It is assumed that all nodes will be aware of their

distance from the BS. A parameter r is defined to allocate

the BW for real time and non real time traffic.

Maximum Energy Cluster Head (MECH)

Although LEACH was proved to be one of the optimal

protocols, it does not consider the node distribution.

MECH is an improvement over LEACH. It involves three

phases: Setup, steady and forward phases [50]. Initially

every node will broadcast hello packets to its immediate neighbors. If the number of neighboring nodes reaches a

predetermined value NH, the corresponding node becomes

the CH and all its immediate neighbors will become the

members. After backoff time every node will reselect the

CH depending on the signal strength.

Hop-based Energy Aware Routing Algorithm (HEAR)

HEAR algorithm does not consider data combining and

routing overhead reduction [51]. Initially the BS will

collect the information about all the sensor nodes in WSN.

If a node has any information to be communicated to the

BS, a message will be sent to the BS. Depending on the distance, BS determines an optimal hop count. It also

determines the corresponding hops to reach the node.

Energy Efficient Adaptive Multipath Routing (EEAMR)

Multipath routing is one possible way of achieving energy

awareness as it leads to the distribution of load along

multiple paths to the destination. But finding the optimal

number of paths is a vital issue. EEAMR is a low overhead

multipath routing with energy awareness [52]. Generally

node with highest energy and farthest from the

transmission node is selected as the next hop. It involves two phases: Multipath construction phase and data

transmission phase.

Energy Efficient Clustering Scheme (EECS)

EECS is a cluster-based approach developed for periodical

data gathering applications [53]. It focuses on low control

overhead and uniform load distribution. Initially BS

broadcasts a HELLO message from which all nodes will

compute their distance from the BS. In CH formation

phase, nodes that are interested to become CHs will

advertise a message within its radio range. A node with higher energy level is elected as the CH for the

corresponding cluster. Other members of the cluster will be

decided based on the distance from the CH.

Minimum Energy Relay Routing (MERR)

MERR takes the linear topology of the network into

consideration. Here it is assumed that the sensor nodes

transmit the data to the base stations via relay nodes and

the distance between the relay nodes has to be approximately equal to the characteristic distance [54].

Characteristic distance is the optimal distance which is a

constant predetermined at the time of network set up.

Energy Aware Routing (ERP)

Energy aware routing uses energy availability and the

received signal strength from the nodes to determine the

optimal path. Each of the nodes will decide the next node

to which the data has to be forwarded based on its residual

energy and the signal strength. The algorithm operates in

three phases: Neighbor Discovery, Route Reply and Reliable Transmission [55, 56]

Transmission Power Control MAC Protocol (SMAC)

Energy consumption can be reduced by reducing the idle

time of the sensor nodes. In SMAC, nodes form virtual

clusters based on common sleep schedules to reduce

control overhead and enable traffic-adaptive wakeup [57].

Cross Layer MAC (CLMAC) Protocol

Reducing the size of the routing table will minimize the

consumption of the energy. CLMAC protocol includes

routing distance in the preamble field [58]. As the usage of big routing tables has been replaced by a field in the

preamble, the traffic in the network can be reduced thus

reducing the energy consumption.

Energy Efficient Cluster Head Selection Algorithm

(EECSA)

EECSA is a cluster based routing algorithm [59]. The

algorithm works in three phases: CH selection, cluster

formation and the scheduling based on TDMA. CHs are

selected based on the residual energy. Proposed algorithm

is an improvement over LEACH. If the available energy of the node is greater than the 50% of the initial value then the

normal LEACH is used else the proposed protocol is used

in which the probability of the node to be selected as a CH

is the ratio of the residual energy and the initial energy.

Simple Energy Efficient Routing Protocol (SEER)

In [60] a flat algorithm has been proposed to improve the

network lifetime. SEER reduces the overall traffic in the

network thus decreasing the energy consumption. In the

initialization phase, BS will broadcast the packet. All nodes

receiving it will update with hop count and re broadcast to its neighbors by replacing the source address by itself and

enters its residual energy. For forwarding node selects its

neighbor with the hop count less than itself. If more nodes

have the same minimum hop count, the one with maximum

energy is selected. Same process is repeated at the

intermediate nodes.

Energy-Efficient Multipath Routing Protocol (EEMR)

EEMR is multipath routing [61]. When there will be an

event, the surrounding nodes exchange data themselves and one of them is chosen as the source. Source aggregates the

data and sends to the BS. Each node selects its next hop

depending on the distance and the residual energy. It is

assumed that multipaths are disjoint. EEMR involves four

phases: Initialization, multipath selection, data

transmission and path maintenance.

Energy Efficient Clustering Algorithm (EECA)

In EECA, CHs are distributed evenly in the network and

unnecessary CHs are avoided [62]. Clusters have to be

formed in the network in such a way that there will an uniform distribution of the CHs. Advertisement message

can be broadcasted based on CSMA protocol. Once the

CHs are elected, clusters will be formed.

Reactive Energy Decision Routing Protocol (REDRP)

REDRP is a reactive energy aware protocol [63]. It

involves four phases viz Initialization, route discovery,

data transmission and route maintenance. Initially the BS

broadcasts a timer packet, each node records the time

stamp at the distance field as less is the delay near is the

node to the BS. Route is established only when there is an

event.

SeNsOr netWork CLUSTERing (SNOW)

SNOW is a cluster-based algorithm in which the nodes

with higher residual energy are chosen to be the CHs. After

CHs are formed, the BS selects the region heads (RH)

amongst the CHs. Nodes with higher residual energy will

be chosen as the region heads. After receiving the

intimation from the BS, each of the CHs check whether it

is a RH and accordingly it will set its region ID. CHs and

RHs are chosen so as to distribute the load amongst them

[64].

Energy Efficient Dynamic Clustering (EEDC)

EEPA is an energy efficient protocol based on the metric

[65]. Initially nodes with higher energy form the CHs and

the remaining nodes become the members. Clusters are

formed so as to minimize the distance between the cluster

members and the CH. Cluster updating is done in the same

manner as that of LEACH [24].

Energy Efficient Geographic Grid Routing (EEGGR)

EEGGR considers the BS to be mobile. Sensor nodes are considered to have location awareness. Grid structure is

used for forwarding the data to the sink node [66].

Energy Aware Directed Diffusion (EADD) Protocol

The normal Directed Diffusion algorithm will always

considers the shortest path between the source and the sink,

which leads to unbalanced energy distribution. In EADD

protocol, if two sources receive the same interest message,

both the nodes will respond to the destination via different

paths [67]. The path with the maximum residual energy is

chosen for communication. If more than one path are having the same available total energy, then the path

involving the highest minimum energy will be selected.

Energy Efficient Routing Scheme for Mobile Wireless

Sensor Networks (MLEACH)

Its an improvement over existing LEACH algorithm which

incorporates the mobility of the sensors. It is assumed that

all nodes are homogeneous and position aware and the BS

is stationary [68]. The total sensing area is divided into sub

areas and CH is optimized within the sub areas. Generally

nodes with less mobility are preferred to the CH.

Color-theory-based Energy Efficient Routing (CEER)

CEER is a cluster based algorithm based on color theory

based localized algorithm. Location of a mobile node is

represented by its RGB values. Server computes the

positions of the nodes depending on the RGB information.

When a node moves to a new location, it collects the RGB

information from its neighbors. CEER involves three

phases viz setup phase, data dissemination phase and

refinement phase [69].

Extending Lifetime of CH (ELCH) ELCH is a hybrid type protocol in which the direct

communication is used for any intra cluster transmission

and multihop is used for inter cluster communication. It

uses MTE (Minimum Transmission Energy) protocol as

the underlying protocol. It involves two phases: setup

phase and steady state phase [70].

Hybrid Energy Aware Routing Protocol (HEARP)

In [71] a new protocol was proposed which combines the

features of LEACH and PEGASIS. In HEARP members in

the clusters will not communicate directly with the CHs but through the intermediate nodes. It involves two phases:

initialization or set up phase and the steady state phase

Global Simulated Annealing Genetic Algorithm

(GSAGA)

GSAGA is a centralized control algorithm which involves

two phases: Setup phase and steady state phase [72].

Initially all nodes will transmit the location information

and the residual energy to the BS. BS will then compute

the average network energy. Nodes with energy more than

the average can become the CHs. Genetic algorithm is used for the cluster head selection

Advanced Medium Access Control (A-MAC)

A-MAC is a TDMA-based MAC protocol, which uses a

distributed technique where node selects its own slot by

collecting the information from its neighbors [73]. A-MAC

has four states in its operation namely initial, wait,

discover, and active states

Track-Sector Clustering (TSC) TSC is a cluster-based algorithm in which the network is

divided into concentric circular tracks and triangular

sectors [74]. BS does the formation of the tracks and

clusters. TSC uses tracks and sectors to form clusters. Head

nodes in each track are selected by the BS. A node is

selected randomly as the head node in level 1. Nodes with

the similar slopes will be chosen as the head nodes in the

higher levels.

Partition LEACH Algorithm (PLEACH)

In [75] an improvement over the LEACH algorithm was proposed: PLEACH. It first does the optimal partitioning

of the network and then the node with the highest energy in

each partition will be chosen as the CH. It outperforms

LEACH as the CHs are evenly distributed over the network

[76].

Energy Aware Adaptive Clustering (EAAC)

Cluster based algorithms were proved to be better

compared to flat routing. EAAC is one such algorithm in

which CHs and the next heads are determined based on the

residual energy and the distance between the CH and the

members in the cluster [77]. EAAC protocol works in various rounds each has a set up phase and a steady state

phase.

Energy-Level Passive Clustering (ELPC)

ELPC is a passive clustering algorithm in which clusters

are formed on demand. It focuses on two issues viz:

minimizing the energy per packet and uniform load

balancing [78].

MiSense Hierarchical Cluster-Based Routing Algorithm

(MiCRA) MiCRA is an extension over the HEED protocol [79]. It

involves two levels of cluster hierarchy. First level CHs

elects the second level CHs. MiCRA considers two

parameters viz residual energy of the nodes which is used

to select the CHs and the intra cluster communication costs

used to break ties.

GRAdient Cost Establishment (GRACE)

GRACE is a dynamic routing algorithm in which BS

initializes the set up phase by sending an advertisement

packet. Routing table is updated at all nodes depending on the energy and distance parameters. Each of the nodes will

then forward the packet from the source node to the node

with the minimum cost [80]

Sensor system for Hierarchical Information gathering

through Virtual triangular Areas (SHIVA)

In SHIVA both nodes and BS are mobile [81]. Although

the nodes are mobile, it is assumed that the logical cluster

remains same for certain duration. It is assumed that BS can predict its movement for the next time interval. The

network topology can be computed by the BS by predicting

its and the nodes movement profile. This topology remains

valid for the time interval under consideration.

Improvement on LEACH Protocol (VLEACH)

Many improvements over LEACH have been proposed.

VLEACH is one such protocol [82]. In VLEACH there

will be a CH, a vice CH and cluster members. Vice CH

will become the CH if the current CH dies. Provisioning of

the vice CH is the key parameter in VLEACH.

Energy Efficient Heterogeneous Clustering (EEHC)

Cluster based routing is more advantageous compared to

flat routing. EEHC is a heterogeneous cluster based

routing. We can categorize the nodes present in the

network as normal nodes with limited energy and advanced

and super nodes with higher energy. Weighted probability

is considered for the election of the CHs [83].

Energy Efficient Routing Algorithm for Hierarchically

Clustering (ERHC)

ERHC is a cluster-based algorithm. Hop count from the BS is considered to form the hierarchy and the CHs are

selected in an autonomous manner. Alternative sensor node

for all intermediate nodes is determined in this algorithm

where the determined node will become the next

alternative intermediate hop if the energy of the present

hop goes below the threshold [84].

Energy Aware DSR (EADSR)

Traditional DSR can be slightly modified to incorporate the

energy function. The basic idea behind this is as follows:

when an intermediate node in the network decides to forward a RREQ message that it has received, it introduces

an additional delay before re-transmitting this message.

The delay is dependent on the residual battery power in the

node. Thus the nodes with higher battery levels are more

likely to be included in the path [85].

Energy Efficient Clustering Hierarchy and Data

Accumulation (EECHDA)

In the protocol proposed in [86], cluster head performs the

communication with the base station. It involves two

phases viz Cluster head election phase and data transfer phase. After some time slots a non CH with higher energy

becomes the new CH.

An Adaptive Energy Efficient Reliable Routing Protocol

(AEERRP)

In the AEERRP, the source adjusts the flooding rate

depending on the loss rate at the sink [87]. If the loss rate is

very less, then the transmission power can be reduced.

Thus there has to be a tradeoff between the power

consumption and the latency.

Energy-Aware QoS Routing (EAQOS)

Although the proposed energy aware routing protocols

minimizes the energy consumption, they do not work good

for some applications where QoS is also required. EAQoS

is an energy aware routing protocol, which also assures

good QoS [88]. It is a cluster-based protocol where the

cluster formation is done by the command node. In order to

support both real time and non real time traffic, a ratio r is

defined is the initial value set by the gateway and

represents the amount of bandwidth to be dedicated both to

the real-time and non-real-time traffic on a particular outgoing link in case of a congestion.

Energy Efficient Cluster-based Routing Algorithm

(EECRA)

EECRA is a cluster based routing algorithm, which

assumes that the sensors are deployed randomly over the

given geographical area [89]. CH is selected based on two

parameters: residual energy and the node degree. CH

selects the members for the cluster based on the energy and

the distance from itself.

Homogenous Clustering Algorithm (HCA) For cluster based routing, cluster formation and leader

election are two crucial issues. In homogeneous clustering

sensors will be of same hardware and initial battery

capability. Initially BS collects the information about the

location of all nodes and initializes clusters such that all

cluster head selection is uniform throughout the area.

Initially CHs are selected randomly in each zone. New CH

is elected periodically depending on the residual energy

and the relative distance from the current CH [90].

Energy Efficient Cross Layer Routing Algorithm with

Dynamic Retransmission for Wireless Sensor Networks

(E2XLRADR)

In E2XLRADR a cross layer approach is considered which

involves sharing of information amongst layers. Physical,

MAC and network layers are considered. Algorithm

involves five phases [91].

Ant colony optimization is combined with the

Opportunistic Routing Entropy (ACO-TDOP)

Opportunistic Routing is an effective energy aware routing

in which the next relay is selected dynamically for each hop and packet. Key design parameter includes the

selection of the path with minimum delay and higher

energy level. For better performance one has to choose the

next hop which has more energy level, consumes less

energy and is nearby to the sink [92].

Balanced Energy-Aware Routing (BEAR)

Energy aware routing protocols are classified as either

energy saver or energy manager. In BEAR there will be a

trade off between energy balancing and optimal distance [93]. It is an improvement over SEER [60]. A flag is used

in the packet to distinguish the normal and the critical data.

Energy Efficient Routing Protocol (EERP)

EERP is based on the learning automata [94]. It does

efficient flooding which in turn leads to energy efficient

routing. Protocol has two phases: identification phase and

data transmission phase.

Threshold Distributed Energy Efficient Clustering

(TDEEC) Although cluster based routing is found to be better

selection of the cluster head is a crucial issue. TDEEC is a

hierarchical routing in which CH selection is based on the

residual energy of the node and the average energy of the

network [95].

Clustering Technique for Wireless Sensor Networks

(CTRWSN)

In [96] a cluster based algorithm was proposed in which

the CHs are chosen on the rotation based in order to have

uniform energy depletion. It minimizes the energy

consumption for new CH selection after each round by keeping the selected CH for m consecutive rounds. Two

level heterogeneous network with normal and advanced

nodes is considered. There are two phases of operation:

Setup phase and steady state phase.

Location Aware Multi-level Clustering (LAMC)

Multi level clustering algorithms were developed in the

literature. An improvement over EEMC is proposed in

[97]. In LAMC, BS will broadcast a beacon message and

all nodes will reply with their location and the residual

energy. BS will then send the command message and the CHs for level 1 are selected. CHs of level 1 will broadcast

message to all nodes within a certain range and the process

is repeated within the cluster to select level 2 CHs. This

process is continued for a predefined number of times to

have multi level clustering.

Power Aware Multi-level Clustering (PAMC)

In PAMC nodes need not have to have their location

information [97]. BS will broadcast a beacon message. All

nodes reply with the minimum power level to reach the BS

along with the residual energy and the power information. Further process is same as that of the LAMC algorithm

[97].

Distance based Energy Aware Routing Algorithm

(DEAR)

Goal of the DEAR algorithm is to optimize all individual

paths so as to make all nodes to consume energy at the

same rate [98]. It consists of two phases viz route setup and

route maintenance. Whenever there is a data to be

transmitted, source node initiates the route setup phase.

Tree based Energy and Congestion Aware Routing

Protocol (TECARP)

Congestion avoidance is more efficient than congestion

control as congestion control requires more resources.

TECARP is a hierarchical routing protocol which

considers the energy awareness through congestion control

[99]. It focuses on congestion avoidance by constructing

the tree efficiently. It involves three phases: network

clustering, creating routing tree and data forwarding.

Energy Efficient Grid Clustering (EEGC) EEGC is a cluster based energy aware routing protocol. It

involves two basic principles: path with minimal energy

consumption and load balancing. EEGC normalizes the

clustering area. It overcomes the drawback of uneven area

distribution for the clusters in the LEACH algorithm [100].

A Tree Based Routing Protocol (TBRP)

TBRP is an energy aware routing protocol proposed for

mobile sensor networks. In this protocol all nodes in the

network form a tree with different levels. Distance between

levels is equal to the radio communication range. The

algorithm involves three phases: tree formation, data

collection and transmission and purification phases [101].

Cluster Based Energy Efficient Routing Protocol

(CBERP)

Although LEACH is an efficient algorithm proposed for

WSNs, CHs will die earlier than other nodes [24]. CBERP is an improvement over LEACH. BS selects the CHs

initially. Multihop transmission using chain of CHs is done

in CBERP [102].

Optimal Path Energy Efficient Routing (OPEER)

Although EEAODV [41] was proved to perform better, it

has been still improved by assigning the job of route

establishment to the BS in OPEER [103, 104]. As the BS is

not energy constrained, it can be over burdened without

affecting the network performance. Also the usage of

multiple thresholds has been proposed in the paper, which further assures the uniform load balancing in the network.

IV. Comparative Analysis

In this section we will be presenting a qualitative analysis

of the protocols discussed in Section III. The comparison

has been made considering various network parameters like the delay, routing overhead, QoS, type of routing

protocol etc.

TABLE 1 COMPARATIVE ANALYSIS OF THE ENERGY AWARE ROUTING PROTOCOLS

Protocol Classification Data

Aggrega

tion

Scalability

Query

Based

Overhead

Delay Position

Estimation

Mobility Working

Layer QoS

LEACH

[24]

Hierarchical/

Cluster based

Yes Limited No Less Less Relative

positions are

considered

Stationary Network Good

GAF

[25]

Flat No Good NA Depends on

the

underlying

protocol

Depends on

the underlying

protocol

Relative

positions are

considered

Depends on

the

underlying

protocol

MAC/

Network Good

GEAR

[26]

Flat No Good No Less Slightly high Considered Stationary Network Limited

TEEN

[27]

Hierarchical/

Cluster based

Yes Limited Yes

(Reacti

ve)

Less Depends on

the threshold

Relative

positions are

considered


EER

[28]

Flat Yes Yes No Less Less No Stationary Network Good

PEGASIS

[29]

Hierarchical/

Cluster based

Yes Limited No Less Slightly high Relative

positions are

considered

Stationary Network Limited

EBRP

[30]

Flat Yes Good Yes Slightly

high

Less Considered Stationary Network Limited

EARMG

[31]

Flat NA Good Yes Slightly

high

Slightly high Considered Mobile Network Limited

HEED

[32]

Hierarchical/

Cluster based

Yes Limited No Less Less Considered Quasi

Stationary

Network Good

GSP

[33]

Flat No Very Good No Very Less Less No Stationary MAC/

Network Good

EARP

[34]

Flat

No No Yes Less Less No Stationary Network Good

RAW-E

[35]

Flat

No Limited No More Less Relative

positions are

considered

Stationary MAC and

Network Limited

CODE

[36]

Flat No Good Yes Slightly

high

Slightly high Considered Mobile Network Limited

SIDE

[36]

Flat Yes Good Yes Less Slightly high Considered Stationary Network Limited

IWCA

[37]

Hierarchical/

Cluster based


positions are

considered

Mobile Network Limited

GACA

[37]

Hierarchical/

Cluster based

Yes Limited No Slightly

high

Less Relative

positions are

considered


BCDCP

[38]

Hierarchical/

Cluster based


high

Less Relative

positions are

considered

Stationary Network/

MAC Limited

CBEERP

[39]

Hierarchical/

Cluster based


positions are

considered


OEER

[40]

Flat No Limited No Less Less No Stationary

Network Good

EE AODV

[41]

Reactive/ Flat

No Yes Yes More More No Stationary Network Limited

EADV

[42]

Flat

No Yes Yes More Can be high Can be

considered

Dynamic

network

Network Limited

MTECH

[43]

Hierarchical/

Cluster based

Yes Limited Yes Less Less Relative

positions are

considered

Can be

mobile

Network Limited

EECRA

[44]

Hierarchical/

Cluster based

Yes Good No Slightly

high

Less Relative

positions are

considered


REAR

[45]

Reactive No Good Yes Slightly

high

Slightly high No Stationary Network/t

ransport Limited

EECCA

[46]

Hierarchical/

Cluster based

Yes Limited Yes Slightly

high

Slightly high Considered Stationary Network Limited

DEEC

[47]

Hierarchical/

Cluster based


high

Less Relative

positions are

considered


ECA

[48]

Hierarchical/

Cluster based


positions are

considered


DPDD

[49]

Hierarchical/

Cluster based


high

Less for real

time traffic

Considered Stationary Network Very

Good

MECH

[50]

Hierarchical/

Cluster based

Yes Limited No More Slightly high Relative

positions are

considered

Not

considered

MAC and

Network Limited

HEAR

[51]

Flat

No Yes No NA Can be high No Stationary Network Good

EEAMR

[52]

Flat/multipath Yes Good No Less Less No Stationary

Network Good

EECS

[53]

Hierarchical/

Cluster based


high


MERR

[54]

Flat

No Limited Yes Less Less No Stationary Network Depend

on dmean

ERP

[55, 56]

Flat No Yes No Less More Relative

positions are

considered


SMAC

[57]

Flat No Yes No More Less No Stationary MAC Good

CLMAC

[58]

Flat

No Yes No Very less Less No Stationary MAC &

Network Limited

EECSA

[59]

Hierarchical/

Cluster based


positions are

considered


SEER

[60]

Flat No Good No Slightly

high

Less No Stationary

Network Limited

EEMR

[61]

Flat/multipath Yes Good No Less Less No Stationary Network Good

EECA

[62]

Hierarchical/

Cluster based


positions are

considered

Stationary MAC/

Network

Good

REDRP

[63]

Reactive No Good Yes Slightly

high

Slightly high No Stationary Network Limited

SNOW

[64]

Hierarchical/

Cluster based


high


EEPA

[65]

Hierarchical/

Cluster based


high

Less Relative

positions are

considered

Stationary Network/

MAC Limited

EEGGR

[66]

Flat Yes Good Yes Less Slightly high Considered Mobile Network Limited

EADD

[67]

Flat

No Yes Yes More More No Stationary Network Limited

MLEACH

[68]

Hierarchical/

Cluster based

Yes Limited No Less Less Considered Nodes are

Mobile but

not BS

Network Good

CEER

[69]

Hierarchical/

Cluster based


high

Slightly high Considered


ELCH

[70]

Hierarchical/

Cluster based


high

Less Relative

positions are

considered


HEARP

[71]

Hierarchical/

Cluster based


positions are

considered


GSAGA

[72]

Centralized/

Cluster based


high

Less Relative

positions are

considered


A-MAC

[73]

Data centric

NA NA Yes NA Less NA Stationary MAC Good

TSC

[74]

Centralized/

Cluster based


high


PLEACH

[75, 76]

Centralized/

Cluster based


high


EAAC

[77]

Centralized/

Cluster based

Yes Limited Yes Less

Less Considered Stationary Network Good

ELPC

[78]

Hierarchical/

Cluster based


positions are

considered


MiCRA

[79]

Hierarchical/

Cluster based


high

Less Relative

positions are

considered


GRACE

[80]

Flat No Good Yes Less Slightly high No Stationary Network Limited

SHIVA

[81]

Hierarchical/

Cluster based

Yes Good No Less Less Considered Mobile Network Good

VLEACH

[82]

Hierarchical/

Cluster based


positions are

considered


EEHC

[83]

Hierarchical/

Cluster based


high

Less Relative

positions are

considered


ERHC

[84]

Hierarchical/

Cluster based

Yes Limited Yes Less Less Relative

positions are

considered


EADSR

[85]

Flat No Yes No Less Slightly high No Stationary Network Limited

EECHDA

[86]

Hierarchical/

Cluster based


positions are

considered

Not

considered

Network Limited

AEERRP

[87]

Flat

No Yes Yes Less Less No Stationary Network Limited

EAQOS

[88]

Hierarchical/

Cluster based

Yes Limited No Less Depends on

design

parameter r

Relative

positions are

considered

Stationary Network Depend

on

design

parame

ter

EECR

[89]

Hierarchical/

Cluster based


positions are

considered


HCA

[90]

Hierarchical/

Cluster based

Yes Limited No Slightly less Less BS will have

all location

information


E2XLRA

[91]

Flat No Good No Less Less No Stationary Physical,

MAC &

Network

Good

ACO TDOP

[92]


high

Less No Stationary Network Limited

BEAR

[93]

Flat No Good No Less Less No Stationary

Network Good

EERP

[94]

Flat No Good No Less Less No Stationary

Network Good

TDEEC

[95]

Hierarchical/

Cluster based


high

Slightly high

Relative

positions are

considered

Stationary

Network Limited

CTRWSN

[96]

Hierarchical/

Cluster based


positions are

considered


LAMC

[97]

Hierarchical/

Cluster based

Yes Limited No High Less Yes Stationary Network Limited

PAMC

[97]

Hierarchical/

Cluster based

Yes Limited No High Less Relative

positions are

considered


DEAR

[98]


high

Less No Stationary Network Limited

TECARP

[99]

Hierarchical/

Cluster based


high

Less Relative

positions are

considered


EEGC

[100]

Hierarchical/

Cluster based


high

Less Relative

positions are

considered


TBRP

[101]

Hierarchical Yes Limited Yes Slightly

high

Less Relative

positions are

considered

Mobile Network/

MAC Limited

CBERP

[102]

Hierarchical/

Cluster based


high

Less Relative

positions are

considered


OPEER

[103]

Flat/ Centralized No Good Yes Very Less Less No Stationary Network Good

Table 1 summarizes the behaviour of each of the protocols

studied. A detailed summary has been presented in table 1.

It has been observed from the table that an ample number

of protocols were proposed in the literature [105, 106, 107,

108, 109]. Most of the algorithms were application specific

and may not work fine for all type of application environments. Most of the proposed energy aware

protocols trade off delay and energy efficiency.

V. Conclusion

Routing in WSNs is one of the emerging areas of research.

There are many challenging tasks to be considered while proposing a routing protocol for WSN. Energy awareness

is one of the important parameters the routing protocol

should posses. In this paper a comprehensive survey has

been made on the energy efficient routing protocols and an

analysis has been presented. The comparison has been

made based on various parameters. From the table it is

clear that, there is no clear winner. The selection of the

routing protocol has to be made based on the application.

But we can conclude that cluster based algorithms are

better compared to flat routing.

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K S Shivaprakasha received his BE (Electronics &

Communication) from Bahubali College of

Engineering, Visvesvaraya Technological University

with IX rank to the University and MTech (Digital

Electronics and Communication Systems) from

Malnad College of Engineering, Visvesvaraya

Technological University with I rank to the

University in 2004 and 2007 respectively.

Presently he is pursuing his PhD at National Institute of Technology

Karnataka, Surathkal in the field of Wireless Sensor Networks. He has a

teaching experience of 5 years. Presently he is a Senior Lecturer at

Bahubali College of Engineering, Shravanabelagola, Karnataka, India.

His areas of interest include Wireless Sensor Networks, Mobile Adhoc

Networks, Information Coding Theory and Cryptography. He has more

than 15 publications to his credit.

Muralidhar Kulkarni received his B.E.

(Electronics Engineering) degree from University

Visvesvaraya College of Engineering, Bangalore

University, Bangalore, M. Tech (Satellite

Communication and Remote Sensing) from Indian

Institute of Technology, Kharagpur (IIT KGP) and

PhD from JMI Central University, New Delhi in the

area of Optical Communication networks.

He has 28 years of experience which includes 5 years in industry and 23

years of teaching experience. He has held the positions of Scientist in

Instrumentation Division at the Central Power research Institute,

Bangalore (1981-1982), Aeronautical Engineer in Avionics group of

Design and Development team of Advanced Light Helicopter(ALH)

project at Helicopter Design Bureau at Hindustan Aeronautics

Limited(HAL), Bangalore (1984-1988), Lecturer (Electronics

Engineering) at the Electrical Engineering Department of University

Visvesvaraya College of Engineering, Bangalore (1988-1994) and

Assistant Professor in Electronics and Communication Engineering

(ECE) Department at the Delhi College of Engineering (DCE), Govt. of

National Capital territory of Delhi, Delhi (1994-2008). He has served as

Head, Department of Information Technology and Head, Computer

Center at the Delhi College of Engineering (University of Delhi), Delhi.

Currently, he is a Professor and Head in the Department of Electronics

and Communication Engineering (ECE) Department, National Institute of

Technology Karnataka (NITK), Surathkal, Karnataka, India.

He is currently the Coordinator of the Centre of Excellence for Wireless

Sensor Networks, Dept. of Electronics and Communication Engineeing,

National Institute of Technology Karnataka. Dr. .Kulkarni’s teaching and

research interests are in the areas of Digital Communications, Fuzzy

Digital Image Processing, Optical Communication and Networks, and

Wireless Sensor Networks. He has published several research papers in

the above areas, in national and International journals of repute. For

various contributions his Biography has been listed in the Marquis, Who's

Who in Science & Engineering (2008). He has also authored/coauthored

four very popular books in Microwave & Radar Engineering,

Communication Systems, Digital Communications and Digital Signal

Processing.

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