A Survey on Energy Efficient Data Aggregation Protocols ... · PDF file... Span, TBF, BVGF, GeRaF Data-centric Protocols SPIN, Directed Diffusion, Rumor Routing, ... False Alarm (FA)

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002

© Research India Publications. http://www.ripublication.com

6990

A Survey on Energy Efficient Data Aggregation Protocols for Wireless

Sensor Networks

M. Shanmukhi1 and O. B. V. Ramanaiah

2

1CSE Department, Malla Reddy College of Engineering, Hyderabad, India.

2CSE Department, JNTUH, Hyderabad, India.

Abstract

Wireless sensor networks (WSN) consist of several sensor nodes which sense different parameters of weather, soil, etc,

and send the same to sink/base station. The sensor node has

very limited battery power that is used for sensing, computing,

and communication. Elimination of redundant sensed data and

aggregation of nearest sensor values is necessary in order to

minimize the communication overhead. Hence to conserve the

battery power and thereby enhance the lifetime of a WSN, we

need energy efficient data aggregation mechanisms. Our

objective is to differentiate the comprehensive study of data

representation model in data aggregation protocols based on

energy conservation model using networks based systems. And also intends to study about the various data aggregation

network based protocols in the aspects of various factor such

as Aggregation Method, Resilience to Link Failure, Setup

Overhead, Scalability, Resilience in case of node mobility,

Energy saving method and Timing strategy.

Keywords: WSN, Data Aggregation Protocols, Battery

Conservation and communication overhead.

INTRODUCTION

A Sensor network is a collection of sensor nodes with sensing, computing and communication capabilities. Sensor nodes

have limited battery energy, besides it is not possible either to

recharge or replenish the node battery once it is deployed in

the field [1]. In a deployed WSN, the data gathering and its

radio communication to sink node (base station) consumes

more energy. Hence we need energy efficient data gathering

and processing mechanisms to enhance the network lifetime.

A sensor node is a tiny device that composed of three portions

[2]:

The data is collected from the physical environment

using the sensing subsystem.

Data manipulation and storage using the processing

subsystem.

Data transmission using wireless communication

subsystem.

In Wireless Sensor Networks, the efficient use of energy plays

a vital issue. In order to extend the lifetime of the sensor

nodes, the energy is a very scrimpy segment. The energy

sources may be of “economical” or “non-economical”. The

economical energy conservation is designated as sending/

receiving data, manipulating the queries and promoting the data and queries to its close-by nodes [3]. The non-

economical energy conservation leads to idle listening,

collision, packet loss, overhearing, control packet overhead

and over-emitting the data. The energy savvy technique is mainly subdivided into a Network subsystem and Sensing

subsystem. Here we analyzed the sensing subsystem to

increase the lifetime of the sensor nodes using the data driven

approach named, Data Aggregation [4].

Data aggregation is defined as gathering and aggregating the

sensed data to get the meaningful information. It is a

fundamental processing method to save energy and effective

way for saving the limited resources [5]. The main aim of a

data aggregation protocol is the process of gathering sensed

data and its aggregation in an energy efficient way so that the

sensor network life time is enhanced. Data aggregation eliminates the redundancy and hence reduces the size of the

data to be communicated to the sink node. The Figure 1

illustrates the working procedure of an aggregation algorithm

[6]. The data is collected by the sensor node is given to the

aggregation algorithm. The aggregated data as an output is

communicated to the sink node. Data aggregation eliminates

the redundancy and hence reduces the size of the data to be

communicated to the sink node. Data representation is an

important stage in the data aggregation.

Figure 1: Data Aggregation System [6]

mailto:[email protected]



6991

The paper is unionized as follows: Section 2 describes the

working of Data Aggregation. The various data aggregation

based networks are explored in Section 3. In Section 4, energy

efficient based network protocols are discussed. A new

thought is concluded in Section 5.

DATA AGGREGATION ARCHITECTURE

Figure 2: Architecture of Data Aggregation [1] [2]

The working principle of Data Aggregation is as follows [1]:

i. Select the group of nodes and separate into a cluster.

ii. The cluster should satisfy the following parameters

such as RSSI, TTL, MSRC, bandwidth, battery

consumption to represent the node in a cluster.

iii. On these above said criteria, the Cluster Head (CH)

is selected among the nodes in a cluster.

iv. The CH should be responsible for all nodes in a

cluster. It should handle the newly arrived nodes,

information exchange, information updation and

information transfer. v. The newly arrived node is chosen to be a CH, in case

of global cost is minimum.

vi. If global cost is maximum then nodes in a cluster is

chosen as CH and global cost is reestimated.

vii. In data aggregation process, the queries and its data

from user end are processed and exchanged to the

query processor.

viii. Then these data are gathered by data cube approach

and the base station acquire the aggregated data from

data cube approach.

DATA AGGREGRATION BASED NETWORKS

There are two types of networks namely, Flat Networks and

Hierarchical Networks [7].

FLAT NETWORKS

In Flat Networks, all the sensor nodes are equipped with same

battery power, and play the same role [7] [8]. In this type of

networks, data aggregation is achieved by data centric routing

[9]. In data centric routing the sink node transmits query

message to the sensor nodes via flooding or other

broadcasting techniques. The sensor nodes which have the data matching with the query message send reply back to the

sink node.

Figure 3: Flat Network [9]

HIERARCHICAL NETWORKS

The computation and communication burden is high at sink

node in flat networks. Consequently a lot of energy is

consumed. In Hierarchical Networks, special nodes in the

field perform the data aggregation and the aggregated data is

sent to the sink node. All the sensor [8] nodes send their

sensed data to the special nodes based on the query received.

The special nodes reduce the data communicated to the sink

by the use of aggregation technique. This results in the

reduction of energy consumption. Hence, energy is utilized

efficiently in Hierarchical Networks. As a result network life

time is increased [10].

Figure 4: Hierarchical Networks [10]

Table 1: Difference between Flat and Hierarchical Networks

based on the parameters [10]

Parameters Flat Networks Hierarchical

Networks

Data Aggregation

performed by the nodes along the path to sink

Performed by Leader nodes or

CHs

Overhead On the nodes in the

Communication path to the

Sink

Only on CH

Fault

Tolerance

if the sink node fails it

results in the breakdown of

the entire network

Even if CH fails the

network can still be

in operation

Latency Latency is high because the Latency is low



6992

communication to sink via

multi hop link

because

communication to

the sink through CH

Routing Optimal routing with

guaranteed overhead

Routing is simple

but may not be

optimal

Node

Heterogeneit

y

It cannot utilize node

heterogeneity for

improving energy efficiency

Node heterogeneity

can be used to

assign high energy nodes as CHs

ENERGY EFFICIENT ROUTING PROTOCOLS

Routing protocols are very important phase in supporting the

Data Aggregation process. The objective of the data

aggregation is to lessen the energy consumption. In accord to

promote the network aggregation, the sensor nodes should

track the packets based on content of the data packets and also

select the next hop [10]. Since there is no specific

infrastructure in WSNs, the sensor node should meet the

energy saving requirements. Routing protocols developed in WSN for energy efficient is tabulated based on seven

categories:

Table 2: Routing Protocols in WSN [10]

Category Representative Protocols

Location

based

protocols

MECN, SMECN, GAF, GEAR, Span, TBF,

BVGF, GeRaF

Data-centric

Protocols

SPIN, Directed Diffusion, Rumor Routing,

COUGAR, ACQUIRE, EAD, Information-

Directed Routing, Gradient Based Routing,

Energy-aware Routing, Information-Directed

Routing, Quorum-Based Information

Dissemination, Home Agent Based Information Dissemination

Hierarchical

Protocols

LEACH, PEGASIS, HEED, TEEN, APTEEN

Mobility-

based

Protocols

SEAD, TTDD, Joint Mobility and Routing,

Data MULES, Dynamic Proxy Tree-Base Data

Dissemination

Multipath-

based

Protocols

Sensor-Disjoint Multipath, Braided Multipath,

N-to-1 Multipath Discovery

QoS-based

protocols

SAR, SPEED, Energy-aware routing

LOCATION BASED PROTOCOLS

The sensor nodes are covered by their locations is known as

location based protocols. The distance between two nodes in a sensor networks is estimated for its location information.

Here, we reviewed some of the protocols designed based on

the location-oriented routing protocols.

Li and Joseph Y. Halpern [11] proposed a method,

Subnetwork that obtains a minimum energy path between the

nodes. They computed at least one minimum energy path

between the node pairs to enhance the energy saving process.

In grid based architecture, the energy consumption is handled

by improving its communication security and cost by

Melkang Qlu et al [12]. The Access Points (AP) is also used

between the node communications. Each node possesses some

energy and this energy is harvested for further use was

proposed by Liang Liu et al [13]. In Multihop framework, the

data packets are transferred between the nodes from Cluster Header. Using the tree based deployment approach the energy

transmission ratio is kept fixed which tries to solve the NP-

complete problem [14]. When minimum energy is deployed,

the maintenance cost is increased simultaneously [15]. To

efficiently utilize the energy at low cost, a segment in data

link acquires Automatic Repeat Request (ARQ) protocol is

utilized. The concept of virtual Multiple Input Multiple

Output (MIMO) in a cross layer approach [16] can also used

to reduce overall energy consumption in per packet

transmission is modeled. The centralized algorithms are

employed to localize the energy consumption [17]. A one-to-

many protocol in dense networks is used to transfer the information between nodes using transmission power

limitation.

The broadcasting process is usually consumes a lot of energy.

It can also optimized by reducing the retransmissions rate by

maximizing the hop length. Without the neighbor information,

optimizing the broadcast networks leads to low

communication and memory overhead [18] [20] [21]. A kind

of trust management is employed between the nodes and

sensing report is maintained. The target is to reduce global

False Alarm (FA) and Missed Detection (MD). The

probabilities rate should be maintained using data fusion techniques [19] [23][24]. Anis Ouni et al framed an energy

efficient protocol using mesh networks. A linear programming

model is used to show the relationship between the throughput

and energy consumption. Thus the combination of single hop

and multi-hop routes are generated for continuous power

control [25] [26]. In terms of QOS, the Wireless Sensor

Networks in data aggregation process. The node reliability

and transmission distance should be minimized [27]. In tree

based approach, the data transmitting energy is consumed.

Applications such as Cognitive radios, the proper use of

energy efficient is important. The resource allocation

protocols are enabled to obtain a near-optimal with low-complexity channel assignment was initiated and framed by

Kandasamy Illanko et al [28]. Hamed Yousefi et al suggested

the concept of scheduling in the data aggregation techniques

[29]. The idea behind their research is to minimize the latency

time so as to schedule the process and thus in this way the

energy can be saved. He suggested a collision free schedule

with least number of time slots [30].

In a cluster approach, the resource efficiency and

dependability is vital issue in energy saving system. Xiaoyong

Li et al framed a lightweight and dependable trust system to

avoid the effect of malicious nodes. The cluster member or head feedback is avoided due to dependable trust system

which automatically improves the energy-saving [31].

Topology management protocols should be well defined in

utilizing the energy in sleep transitions of the nodes. A static

topology management protocol is efficient in real time WSN

that reduce the bound delay and route fidelity [32]. On the

contrary to static process, dynamic topology management was

initiated to balance the nodes and enhance the network



6993

lifetime by managing event driven data transmissions. The

process of synchronization between the nodes has established

in [33]. Regression analysis is used to estimate the

relationship between two variables. In Multihop architecture,

the sources of errors and data transmission rate are estimated

to find a new relationship between the nodes. The nodes in a cluster may be local or global. A k level hierarchical structure

with low energy localized clustering algorithm established

with parameters such as residual energy of nodes, the

aggregation degree and lifetime of the nodes [34].

Duty cycling is an important issue in densely connected

wireless sensor networks. The local information about the

node is gathered in the geographic oriented system. The k

covered WSNs are estimated by the Cover Sense Inform (CSI)

framework which intends to merge the sensor scheduling and

data forwarding. The high k coverage is actualized by k active

sensors to the sink node to eliminate the intruder detection and

tracking [35]. Data fidelity is also used for energy efficiency in data aggregation schemes. The Compressed Sensing (CS) is

employed to achieve the data fidelity. The diffusion wavelet

characterizes spatial correlated data. The NP complete and

integer programming model is instantiated to solve the

minimum energy compressed data aggregation problem [36].

In cluster oriented architecture, the data collection process is

efficient in handling the communication between the nodes.

The node in sleep/ awake scheduling incurs energy. It is

handled by an adaptive scheme cluster to cluster propagation

scheme to reduce communication cost and limiting prediction

cost [37]. Though there was a lot of energy saving methods that dealt with one to one communication, one to many

communications etc. In [38], the many to many

communications has been proposed using the MUSTER

routing protocol. It has ability to operate in multicast networks

using multiple sources to multiple sinks which concurrently

increase the network lifetime and balance the nodes.

Table 3: Merits and Demerits of Location based Protocols

[38]

Protocol Merits Demerits

Geographic

Adaptive Fidelity (GAF)

Best use of

performance in WSN.

Scalability is good.

Increase the

network lifetime

Restricted energy

consumption

Overhead is high.

QOS is less in data transmission.

Restricted mobility

Restricted power

management.

Geographic and

Energy Aware

Routing (GEAR)

Enhance the

network span.

Reduced energy

consumption

Restricted

versatility.

Restricted mobility

and power

management.

High overhead

QOS is poor.

Coordination of Power saving with

Routing (SPAN)

Consumes less energy.

Less overhead.

Good support for

data aggregation

Restricted versatility.

Communication

overhead is high.

Lack of QOS

process. support.

Trajectory Based

Forwarding (TBF)

Improved

Reliability

Network

management is

proper.

Network perimeter

is secured.

Overhead is high.

Bounded Voronoi Greedy Forwarding

(BVGF)

Easy to implement. High

comprehensive.

Collision in determining

Voronoi partitions.

Geographic Random

Forwarding (GeRaF)

Unsettled nodes.

Lack of creating

multi-hop overhead.

Simple to combine

with awake/ asleep

state to save energy.

Necessary to initiate

the user interaction

in each stage.

Time complexity is

high.

Minimum Energy

Communication

Network (MECN)

Energy

sustainability with

low power.

High fault tolerant

systems. Span time is

optimal.

Based on specific

application, the

fault tolerance

varies.

Small Minimum

Energy

Communication

Network (SMECN)

Incurs less energy

than the MECN.

Less maintenance

costs in supporting

source links.

Power usage is

high.

Size of message

broadcasting is

high.

DATA CENTRIC PROTOCOLS

A data centric protocol is varied from the Address centric

protocols. A sink node forwards the queries to the specific

regions and searches the data in that specific region from the sensors. Each data queries are represented by attribute based

naming strategy [39]. The research conducted on the data

centric protocols are reviewed as follows:

First of all, there should not be any redundant data

transmissions to save the energy. Harshavardhan et al [39]

framed location aided flooding scheme to efficiently utilize

the energy. He used local information about the node which is

used to discards the same packet send by a node is transmitted

in virtual grid based architecture. In dynamic networks, the

node transmission is done in a random manner. Flooding time is estimated to define the node transmission rate. These

information are bounded with high probability with the best

node mobility has framed by Andrea Clementi et al [40].

Though the node is transmitted from source to destination, an

acknowledgement from the receiver is essential to verify

whether the data packets are being forwarded to designated

destination. In [41], a link correlation is defined between the

source and destination nodes. The collective ACK is

calculated in single hop routing with low energy consumption.

The node can also reference to other nodes in the time

synchronization process. Within this context, the

synchronization accuracy and flexibility has been reduced for slow flooding process [42]. Coming to the security issue,



6994

many intruders are there to spoil the systems by introducing a

new node in the system. The Open Network Foundation

(ONF) ensured to decrease the unwanted node transmission.

The Software Defined Networks (SDN) is introduced to have

a control over the control messages and incoming packets to

classify the nodes [43]. The energy maximization is done by restricting the resources

[44]. The smallest distance between the two nodes is a straight

line. These straight lines are used for framing an improved

protocol in WSN. Without the aid of geographic locations, the

straight line routing reduces the energy consumption level.

The power constrained WSN acquires two quantities namely,

broadcast capacity and information diffusion rate [45]. In

Multihop relay, the rate of broadcasting stream is estimated to

discover the asymptotic relationship between unified routing

structure and MAC schedule. The energy consumption is

directly proportional to network performance. A single path

can connect a various nodes [46]. The multipath should discover to solve the issue of control plane problem and data

plane problem so as to enhance the network performance

along with the minimized energy consumption. A key

management system was introduced to enhance the

information security process. The secret key was generated for

edge routers which generate minimal communication [47]. In

diffusion-based molecular nanonetworks, the routing

functionalities play an important. The gradient based

information is obtained between the nodes. The OR function

is utilized to serve the best routing protocols [48].

The error propagation approach is used to display the mutual information on the achievable throughput and throughput-

delay. Sometimes the transmitting nodes may not cooperate

due to interference-limited setting. This delimitation is

improved in [49]. The networks may be noisy that leads to

high power consumption. To overcome this issue, the data

delivery networks should be reliable one. This was solved in

[50]. A flexible cross layer design is maintained to enhance

the link estimation capabilities and efficient management of

neighbor tables that minimizes the power consumption.

Another aspect of energy conservation is the energy balance

routing protocols. The Forward Factor (FF) supports between

the nodes can also establish for the energy balanced mechanism. The selection of the next hop node in accord to

the link weight and predecessor nodes information, the FF

value is computed [51]. In Tree oriented architecture, a node

should be a self organized in case of embedded nodes. The

base station assigns a root node and broadcasts the messages

to all sensor nodes. Each node randomly chooses its parent

and neighbor‟s information to make a dynamic protocol [52].

The scalable nodes are formed in the hierarchical structure for

efficient use of energy. The cluster head is responsible for

network lifetime. When the cluster head fails, the node‟s

position is not properly organized which leads to overhead. This can be eliminated by selecting the CH based on the node

eligibilities [53]. A fuzzy based approach in the CH formation

can reduce the risk of node collision issue. Thus the

optimization of the routing protocols can also achieve in this

[54] [55].

Table 4: Merits and Demerits of Data-centric Protocols [55]

Protocols Merits Demerits

Flooding Easy to implement Node collision is

high.

Resource blindness.

Gossiping It avoids node

collision issue.

Node intersection is

high.

Directed

Diffusion

Data cache prevents

loops in data delivery.

Saves network

bandwidth.

Not suitable in

continuous data streams.

Rumor Routing A single path is

maintained between

source and

destination.

Node failure

handling is efficient.

Lack of handling

events.

Energy aware

routing

Use sub-optimal

paths to increase

network lifetime.

Route setup process is

complicated.

Constrained

Anisotropic Diffusion Routing

(CADR)

Minimize the

latency and bandwidth.

Dynamically adjusts

the data routes.

Node Intersection

problem is high.

COUGAR Best use of

declarative queries.

Data abstraction is

efficient.

Cluster Head must be

maintained in

dynamic.

Communication

overhead is high to

sensor nodes.

Need of

synchronization.

Active Query

forwarding in

sensor networks (ACQUIRE)

Query is very

comprehensive.

If network size is

equal to node size

then it behaves like flooding.

HIERARCHICAL ROUTING PROTOCOLS

This type of routing protocols is treated as the most energy

efficient protocols in WSN because of its higher energy

conservation, network versatility and lower data transmission

[56] [75] [76].

In Zigbee mesh networks [57], the hierarchical protocols are

used to find the shortest path for addressing scheme. This

protocol lessens the broadcast overhead and no memory

overhead to maintain the routing information. The chain based protocol minimizes the energy consumption level because

each node communicates with the next close-by neighbor and

then transmits to the base station. The data gathering process

is reduced to yield the optimal path [58]. The energy should

be dissipated to the BS to improve the network lifetime. The

sensor nodes are arranged in the clustering based schemes to

maximize the life span of the network [59]. According to the

node proximity or node degree, the CH is periodically chosen

that achieves fairly uniform cluster formation and low

message overhead [60] [61]. Route election and route



6995

distribution is an effective task in the energy efficiency issue.

A proper route should be elected for transmitting the nodes

and its information [62]. Nodes under heavy traffic might

cause energy efficiency problems. This problem exists due to

heavy burden to the CH in the data collection scenarios. The

CH size should determine effectively to eliminate the higher level of energy consumption. The data sink contains burden of

the sensory data in the data collection approach [63] [68]. The

hop distance size is estimated for CH in the data sink to

dispose the burdened sensor data. The issues such as

congestion control, node collision and resource blindness

solved in cross layer fashion. The design focused on the initial

determination of nodes in the networks. The initial

determination such as receiver based contention, initiative

based forwarding, local congestion control and distributive

cyclic operation to adopt a reliable communication [64]. In

[65], the false alarm rate in CH formation can enhance the

node scalability. The multiaxis division based approach is introduced to overcome the threshold problem and eliminate

the sink dependency. The data aggregation approach handles a

variety of queries. The Poisson Arrival Rate is estimated to

handle the heavy loads using Time Division Multiple Access

(TDMA) [66] [67].

Table 5: Merits and Demerits of Hierarchical Protocols [68]

Protocols Merits Demerits

LEACH Load balancing is

efficient.

Collision is

prevented.

Fit to small regions.

Energy is not

utilized for selection

of cluster head that leads to overhead.

Power Efficient

Gathering in

Sensor

Information

System

(PEGASIS)

Decreased overhead

in dynamic cluster

formation.

Data transmission

rate is low.

Balanced node

formation.

Not suit to time

variant topology.

Data Processing is

delayed.

Hybrid Energy

Efficient

Distributed

(HEED)

Distributed

clustering method.

CH is uniform.

Maximized energy

conservation. Support long range

communications

Inappropriate energy

consumption.

Cause overhead in

cluster head

selection. Expired soon due to

overload.

Threshold

sensitive Energy

Efficient sensor

Network

Protocol (TEEN)

Energy consumption

is reduced by the use

of thresholds.

Fit to reactive

scenes.

Periodic reports are

not generated.

Data loss.

Adaptive

Threshold

sensitive Energy

Efficient

sensor Network

protocol (

APTEEN)

Applicable to

proactive and

reactive applications.

Controlled energy

consumption

Design complexity

is large in

supporting multi-

path.

MOBILITY BASED PROTOCOLS

Based on the mobility, the cluster head are selected. The node

which possesses less mobility is selected as the cluster head

which leads to more stable clusters. These are also known

energy-unaware routing protocols. There is a chance of packet

loss in inter-cluster communication [69] [70]. In data collection process, energy hole problem is a critical

issue. The sink mobility is an efficient way to eliminate this

issue and extend the lifetime of a network by lessening the

communication overhead which is close to the sink [71] [72]

[77]. The mobile node should be of high fidelity. A

cooperative engagement should be followed in unreliable

wireless networks to effectively coordinate the

communications. The objects/ events are updated frequently

to yield the optimal path [73] [78]. The process of handling

multiple mobile sink is also an issue in data aggregation

process. The next position of the sink is selected by biased

random walk. The rendezvous point acts a threshold value. This significantly reduces the reliability, communication

overhead and flexibility [74] [79]. The beaconless routing

schemes are well defined in dynamic network topology. Each

node transfers the packets without the use of beacons interval

and neighbor information. In forward decision process, the

routing schemes are robust [80]. By encountering the RTS and

CTS message, the packet is unicasted to next hop relay which

reduces the energy consumption. The route should be

optimized in concept of discover-before-forward. The

signaling cost is reduced which directly symbolizes energy

consumption [81] [82]. Without scheduling the direction for a mobile sink ahead of

time, an information gathering convention utilizing versatile

sinks recommends that a versatile sink declare the location

data habitually all through the system. On the off chance that

sensors can anticipate the portable sink's development, the

vitality utilization would be extraordinarily diminished

furthermore, information packets handoff would be smoother.

In dense oriented networks [83], a convention, called a

Scalable Energy-effective Asynchronous Spread (SEAD) is

another system for sense the data to versatile sink. The

thought is to develop a least Steiner tree for the portable sink

and assign some hubs on the tree as access points [84]. The portable sink registers itself with the nearest get to hub. At the

point when the sink moves out of scope of the entrance hub,

the route is reached out through the incorporation of new get

to hub. A stable and high recharging rate power supplies for

sensors, and effectively alleviates energy cost on data

gathering at the same time, [85] proposed a joint design of

energy replenishment and data gathering by exploiting

mobility.

Table 6: Merits and Demerits of Mobility based protocols [85] Protocols Merits Demerits

SEAD Sense the data based on the geographic locations. Self organizing protocol.

Overhead is high

Joint Mobility and Routing

Well balanced nodes. Energy sink-hide problems

Consumes more power.

Data MULES Low cost infrastructures. Less fault tolerant systems.



6996

MULTIPATH BASED PROTOCOLS

The data packets are forwarded to the sink node directly from

the sensor nodes. The sensor nodes consumes more battery, to

eliminate this action „multipath routing protocol‟ is introduced

[74] [75]. In any type of architecture, the node with maximum

energy is processed and the nodes with minimized energy yield more time which leads to heavy computational costs.

The multipath routing protocols produces less scalability and

simplicity.

A hierarchical multipath routing protocol possesses many

paths to exploit data forwarded to the sink. The candidate

parent nodes are created to the sink node based on the layered

network [86]. Some multipath routing eliminates the topology

exposure protocols. The idea is to hide the topology and does

not carry the packets information. The protocol can likewise

set up numerous hub disjoint path in a route invention

endeavor and reject the slower action before transmitting

parcels [87]. In mobile ad hoc networks, the location prediction concurrently reduces the global multipath route and

average of hop size from source to destination is reduced.

Based on the node disjoint paths, the collected location and

mobility information are utilized to reduce the control

message overhead and energy consumption [88]. Frequent

connection disappointments are brought in mobile ad hoc

systems because of hub's portability and utilization of

untrustworthy remote channels for information transmission.

In the route discovery system, the path with optimal

connection quality and path expiration time is chosen as the

essential routes. On the off chance that there is any route failures amid the information transmission through essential

way, the following accessible backup path with high

connection quality and path expiration time is chosen [89]

[93]. To eliminate the traffic of the nodes, multipath routing is

used. The load is balanced among the nodes with the use of

node disjoint paths. It is a sink initiated routing protocol. The

number of hop count and uplink neighbor‟s information,

multiple paths between the source and destination is

discovered [90]. The overhead problem is not recovered in

demand routing protocols [91] [92]. The multipath routing

extracts the benefits of AODV protocols. It discovers three

node disjoint routes from source to destination which enhances the packet delivery ratio and end to end delay. If any

route discovery fails, the backup route helps to transfer the

packets without incurring extra energy [94].

In heterogeneous WSNs, a distributed fault tolerant topology

algorithm is developed. The super nodes are generated

according to k vertex disjoint paths. It reduces the power

consumption and efficient super nodes creation [95] [100]. In

service oriented architecture, the link disjoint based multipath

routing is introduced which eliminates the packet forwarding

to the unwanted nodes. So, each node should possess the

capability of detecting reliable and related nodes [96]. The node coverage issue is solved by deploying the stationary

sensors. A weight barrier graph has been introduced to

determine the minimum number of mobile sensor nodes that

reduces the effect of overhead [97]. There is a chance of

gathering unrelated data from the mobile sensor nodes. In

[98], the authors introduced a concept of resiliency networks

in a distributed manner. A relative indexing is used to reduce

the coding coefficients to cope up with node or link failures.

The actor failure in the network may cause partition tasks into

disjoint blocks. If any node fails, it will consult with the

neighbor to define the role and actions to the network

connectivity [99] [101].

Table 7: Multipath based protocols [101]

Protocols Definition

Disjoint Path Primary preference is given to the smallest path.

If any failure occurs, it remains stagnant.

Braided Path Partially disjoint path.

Primary computation path is done.

It builds in localized manner.

QOS BASED PROTOCOLS

The Quality of Service (QoS) prerequisites varies in the

different applications in the view of delivery ratio and packet

loss. Based on the QoS requirements, the network protocol

should be designed [74] [75].

A cross layer design was introduced [102] to increase the energy consumption and network bandwidth of IEEE 802. 15.

4. In route discovery process, the location of the mobile

sensor is embedded. This information is utilized by the

network layers in later that simultaneously reduce the

transmission range and power consumption. The Cognitive

Radio Networks (CRN) in Federal Communication was also

suffering from this issue. The cognitive radio methods such as

interleave and underlay was designed to meet the Primary

User (PU) QOS requirements [103]. The pair-wise nodes are

utilized to collect the geographical information of the nodes in

data gathering approach that balance the energy consumption

and end to end delay [104] [113]. Actually, the presence of energy holes that exhibit on Primary User (PU) movement is

insufficient to empower transfer speed in the inter-

communication. It additionally needs to consider the Quality

of these gaps [105]. To minimize the interruption to the high-

transmission capacity streams, the spectrum detecting process

needs to recognize stable unmoving channels, i. e., ones that

are relied upon to stay unmoving for a broadened span of

time. Different levels of QoS requirements needed for smart

grid which symbolizes the packet delay in end to end systems

[106]. Without picking a specific program, the system should

ensure the packet delay quality in a specified range. This study seems to be quite trickier tasks in wireless NAN without

compromising the QoS. In demand routing protocols, the QoS

in terms of packet delay, packet error probability and node

outage issue are solved.

Tuning the data rate of a sensor is also a QoS requirement.

The utility concept, Nash Bargaining Solution (NBS) that

operates on co-operative game theory that saves the power

consumption and network specific parameters [107]. Sensor

hubs are defenseless to different wellsprings of failures. These

incorporate malware assaults, software corruption and

programming defilement which can decrease hubs' role and

seriously influence the most WSN operations. These attacks could prompt discriminating disadvantages, for example,

incomplete or complete hub failures that causes dangerous

impacts on the fundamental observing applications. Hubs

encountering such failures or glitch can be named



6997

contaminated and will typically neglect to perform normal

detecting and communication [108]. Consequently, they are

not able to watch the auspicious time delivery service which is

critical to look after high QoS in WSN. This is handled by

fuzzy based approach in twofold. The network with light load

possesses high sensitive and high integrity applications. And these can lead the system from congestion problem which

enhances the end to end delay. The concept of potential field

can reduce the end to end delay [109] [112]. In cluster tree

based data collection, a velocity based data collection scheme

is introduced [110]. This reduces the issues of coverage

distance, mobility, traffic delay and tree density. It works on

collecting the information from the CH and transfer to the

sink. Coming to the Resource management and Resource

allocation in terms QoS requirements, the maximum and

minimum number of channels is utilized. To overcome this

issue, the integer programming and convex optimization

method is utilized [111]. It reduces the effects of imperfect channel sensing by generating co-tier interference between the

nodes.

Table 8: QoS-based protocols [113]

Protocols Definition

Core Extraction Distributed Ad

hoc routing (CEDAR)

It‟s a medium sized routing

scheme.

Dynamic Networks

Routing computing is

simple.

Less overhead.

Multipath Routing Protocol

(MRP)

Reactive on-demand

routing protocol.

Higher reliable nodes.

Well defined load

balancing schemes.

Ad hoc QoS on demand routing

(AQOR)

Limited flooding process.

Smallest rate of end to end

delay. Reduces communication

overhead.

CONCLUSION

Data aggregation in sensor networks has attracted a lot of

attention in the recent time. In this paper, we summarized

recent research results on data aggregation and routing in

WSN. We surveyed routing protocols by taking into account

several classification criteria, including location information,

network layering and in-network processing, data centricity,

Mobility-based, Multipath-based Protocols, network heterogeneity, and QoS requirements. The Table 9 shows the

summary of the Basic characteristics of Data Aggregation

protocols. If we consider the parameters Aggregation Method,

Resilience to Link Failure, Setup Overhead, Scalability,

Resilience in case of node mobility, Energy saving method,

and Timing strategy, the Cluster based data aggregation

protocols perform well compared with other protocols and we

can build energy efficient WSN with these protocols.

Table 9: Summary of the Basic characteristics of Data Aggregation protocols

CHARACTERISTIC

ALGORITHM TAG [118]

DIRECTED

DIFFUSION [114] PEGASIS [117] DB-MAC[119] LEACH [116] COUGAR [115]

COMB-

NEEDLE

MODEL [120]

CLUSTER BASED

COMB NEEDLE

MODEL [121]

Aggregation Method

Tree Based

Online Driven

by the sink

Tree Based Online

Driven by the sink

Chain Based

Centralized or

Distributed

Completely

distributed,

asynchronous

Cluster Based

online

distributed

Cluster Based

online distributed

synchronous

Grid Based Cluster Based

Resilience to Link

Failure Medium Medium Low Medium Low Medium Medium Low

Overhead to

setup/maintain the

Aggregation Structure

High High High Low Medium Medium High Low

Scalability Low Medium Very Low High Low Low High High

Resilience in case of

node mobility Low Medium Very Low High Low Low Medium Low

Energy saving methods Sleeping

Periods None

Rotation of the

Leader None

Rotation of the

CH, Sleeping

Periods

Local Route

repairs None

Placing high

Energy nodes

as CH

Timing Strategy Periodic per

Hop adjusted Asynchronous Periodic per Hop Asynchronous

Periodic per

Hop Periodic per Hop

Event

Driven

Periodic and

event Driven

Although these protocols promise the energy efficiency, further research is required to address issues such as quality of

service (QoS) in real-time applications. The applications

include real time military applications, event triggering in

precision agriculture etc. require energy-aware QoS routing.

This will guarantee the bandwidth of connection as well as

provides the energy efficient path.

Further possible research issue for routing protocols is to

consider the node mobility. Most of the protocols we

discussed above (except cluster-based comb-needle model)

assume all the sensor nodes and sink are stationary. Further

research is necessary in this direction to design energy efficient protocols specific to a particular application. For

example, in battle field it may be necessary for the sink to

move for detecting the events such as identification of enemy

tank, exhaustion of ammunition at a soldier, etc. The mobility

of a node requires more number of updates in terms of its

position, and propagation of that information to other nodes

may consume more energy. The battery of the nodes may

drain out fast. To handle this situation and overhead of the

mobility, new routing protocols need to be designed in an

energy constrained environment.



6998

REFERENCES

[1] Sumit Chaudhary et al, “Energy Efficient Techniques

for Data Aggregation and Collection in WSN “,

International Journal of Computer Science, Engineering and Applications, Vol. 2, No. 4, August 2012.

[2] Mousam Dagar and Shilpa Mahajan, “Data

Aggregation in Wireless Sensor Network: A

Survey”, International Journal of Information and Computation Technology, ISSN 0974-2239 Vol. 3,

No. 3, 2013, pp. 167-174.

[3] Ankit Tripathi et al, “Survey on Data Aggregation

Techniques for Wireless Sensor Networks”,

International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3,

Issue 7, July 2014.

[4] Priya V. Ujawe and Simran Khiani, “Review on Data Aggregation Technique for Energy Efficiency in

Wireless Sensor Networks”, International Journal of

Emerging Technology and Advanced Engineering,

Vol. 4, Issue 7, July 2014.

[5] Sushruta Mishra and Hiren Thakkar, “Features of

WSN and Data Aggregation techniques in WSN: A

Survey”, International Journal of Engineering and

Innovative Technology (IJEIT) Volume 1, Issue 4,

April 2012.

[6] Rajashree V. Biradar et al, “classification and

comparison of routing protocols in wireless sensor networks”, Special Issue on Ubiquitous Computing

Security Systems, Vol. 4.

[7] Nandini. S. Patil and Prof. P. R. Patil, “Data

Aggregation in Wireless Sensor Network”, IEEE

International Conference on Computational

Intelligence and Computing Research, 2010.

[8] Shio Kumar Singh et al, “Routing Protocols in

Wireless Sensor Networks –A Survey”, International

Journal of Computer Science & Engineering Survey

(IJCSES) Vol. 1, No. 2, November 2010.

[9] Kiran Mariya et al, “Wireless Sensor Network: A

Review on Data Aggregation”, International Journal of Scientific & Engineering Research Volume 2,

Issue 4, April-2011.

[10] C. Intanagonwiwat, R. Govindan, and D. Estrin,

“Directed Diffusion: a Scalable and robust

communication paradigm for sensor networks”, in: Proceedings of the 6th Annual ACM/IEEE International conference on Mobile Computing and Networking (MobiCom’00), Boston, MA, August

2000.

[11] Li Erran and Joseph Y. Halpen, “A Minimum-

Energy Path-Preserving Topology-Control Algorithm”, IEEE Transactions on wireless

communications, Vol. 3, No. 3, May 2004.

[12] Melkang Qlu et al, “Balance of Security Strength and

Energy for a PMU Monitoring System in Smart

Grid”, IEEE Communication Magazine, 2012.

[13] Liang Liu et al, “Multi-Antenna Wireless Powered

Communication with Energy Beamforming”, IEEE

Transactions on communications, Vol. 62, No. 12,

December 2014.

[14] Kai Han et al, “MEGCOM: Minimum-Energy Group

COMmunication in Multihop Wireless Networks”,

IEEE Transactions on Vehicular technology, Vol. 63,

No. 4, May 2014. [15] Marco Tacca et al, “Cooperative and Reliable ARQ

Protocols for Energy Harvesting Wireless Sensor

Nodes”, IEEE Transactions on wireless

communications, Vol. 6, No. 7, July 2007.

[16] Yong Yuan et al, “Virtual MIMO-Based Cross-Layer

Design for Wireless Sensor Networks”, IEEE

Transactions on vehicular technology, Vol. 55, No.

3, May 2006.

[17] Zi-Tsan Chou et al, “Optimal Asymmetric and

Maximized Adaptive Power Management Protocols

for Clustered Ad Hoc Wireless Networks”, IEEE

transactions on parallel and distributed systems, Vol. 22, No. 12, December 2011.

[18] Melike Erol Kantarci et al, “Suresense: Sustainable

Wireless Rechargeable Sensor Networks for the

Smart Grid”, IEEE Wireless Communications, June

2012.

[19] Julien Iguchi Cartigny et al, ” Localized Minimum-

Energy Broadcasting for Wireless Multihop

Networks with Directional Antennas”, IEEE

Transactions on computers, Vol. 58, No. 1, January

2009.

[20] Arjan Durresi et al, “Optimized Broadcast Protocol for Sensor Networks”, IEEE Transactions on

computers, Vol. 54, No. 8, August 2005.

[21] Seyed Ali et al, ” Energy Efficient Collaborative

Spectrum Sensing Based on Trust Management in

Cognitive Radio Networks”, IEEE Transactions on

wireless communications, Vol. 14, No. 4, April 2015.

[22] Volkan Rodoplu et al, ” Minimum Energy Mobile

Wireless Networks”, IEEE journal on selected areas

in communications, Vol. 17, No. 8, August 1999.

[23] Shibo He et al, ” EMD: Energy-Efficient P2P

Message Dissemination in Delay-Tolerant Wireless

Sensor and Actor Networks”, IEEE journal on selected areas in communications/supplement, Vol.

31, No. 9, September 2013.

[24] Ming Wei Wu et al, “ARQ with Channel Gain

Monitoring”, IEEE transactions on communications,

Vol. 60, No. 11, November 2012.

[25] Hina Tabassum et al, “On the Spectral Efficiency of

Multiuser Scheduling in RF-Powered Uplink Cellular

Networks”, IEEE transactions on wireless

communications, Vol. 14, No. 7, July 2015.

[26] Anis Ouni et al, “Energy and Throughput

Optimization of Wireless Mesh Networks with Continuous Power Control”, IEEE transactions on

wireless communications, Vol. 14, No. 2, February

2015.

[27] Jun Long et al, ” Reliability Guaranteed Efficient

Data Gathering in Wireless Sensor Networks”, IEEE

special section on industrial sensor networks with

advanced data management: design and security,

2015.



6999

[28] Kandasamy Illanko et al, “Energy-Efficient

Frequency and Power Allocation for Cognitive

Radios in Television Systems”, IEEE systems

journal, 2015.

[29] Hamed Yousefi et al, “Fast Aggregation Scheduling

in Wireless Sensor Networks”, IEEE transactions on wireless communications, Vol. 14, No. 6, June 2015.

[30] Fangming Liu et al, “AppATP: An Energy

Conserving Adaptive Mobile-Cloud Transmission

Protocol”, IEEE transactions on computers, 2015.

[31] Xiaoyong Li et al, ” LDTS: A Lightweight and

Dependable Trust System for Clustered Wireless

Sensor Networks”, IEEE transactions on information

forensics and security, vol. 8, no. 6, June 2013.

[32] Lucia Lo Bello and Emanuele Toscano, “An

Adaptive Approach to Topology Management in

Large and Dense Real-Time Wireless Sensor

Networks”, IEEE transactions on industrial informatics, Vol. 5, No. 3, August 2009.

[33] Muhamad Akhlaq and Tarek R. Sheltami, ” RTSP:

An Accurate and Energy-Efficient Protocol for Clock

Synchronization in WSNs”, IEEE transactions on

instrumentation and measurement, Vol. 62, No. 3,

March 2013.

[34] Cesare Alippi et al, “An Adaptive LLC-Based and

Hierarchical Power-Aware Routing Algorithm”,

IEEE transactions on instrumentation and

measurement, Vol. 58, No. 9, September 2009.

[35] Habib M. Ammari, ” CSI: An Energy-Aware Cover-Sense-Inform Framework for k-Covered Wireless

Sensor Networks”, IEEE transactions on parallel and

distributed systems, Vol. 23, No. 4, April 2012.

[36] Liu Xiang et al, “Compressed Data Aggregation:

Energy-Efficient and High-Fidelity Data Collection”,

IEEE/ACM transactions on networking, Vol. 21, No.

6, December 2013.

[37] Hongo Jiang et al, “Prediction or Not? An Energy-

Efficient Framework for Clustering-Based Data

Collection in Wireless Sensor Networks”, IEEE

transactions on parallel and distributed systems, Vol.

22, No. 6, June 2011. [38] Luca Mottola et al, “MUSTER: Adaptive Energy-

Aware Multilink Routing in Wireless Sensor

Networks”, IEEE transactions on mobile computing,

vol. 10, no. 12, December 2011.

[39] Harshavardhan Sabbineni et al, “Location-Aided

Flooding: An Energy-Efficient Data Dissemination

Protocol for Wireless Sensor Networks”, IEEE

transactions on computers, Vol. 54, No. 1, January

2005.

[40] Andrea Clementi et al, “Opportunistic MANETs:

Mobility Can Make Up for Low Transmission Power”, IEEE/ACM transactions on networking,

Vol. 21, No. 2, April 2013.

[41] Ting Zhu et al, “Achieving Efficient Flooding by

Utilizing Link Correlation in Wireless Sensor

Networks”, IEEE/ACM transactions on networking,

Vol. 21, No. 1, February 2013.

[42] Kasim Sinan Yildrim et al, “Time Synchronization

Based on Slow-Flooding in Wireless Sensor

Networks”, IEEE transactions on parallel and

distributed systems, Vol. 25, No. 1, January 2014.

[43] Ying Dar Lin et al, “An Extended SDN Architecture

for Network Function Virtualization with a Case

Study on Intrusion Prevention”, IEEE Network

Communication, 2015. [44] Hsiang-Hung Liu et al, “On Energy-Efficient

Straight-Line Routing Protocol for Wireless Sensor

Networks”, IEEE systems journal, 2015.

[45] Rong Zheng, “Asymptotic Bounds of Information

Dissemination in Power-Constrained Wireless

Networks”, IEEE transactions on wireless

communications, Vol. 7, No. 1, January 2008.

[46] Junaid Qadir et al, ” Exploiting the power of

multiplicity: a holistic survey of network-layer

multipath”, IEEE Communications Surveys &

Tutorials, 2015.

[47] Sudip Mishra et al, “A PKI Adapted Model for Secure Information Dissemination in Industrial

Control and Automation 6LoWPANs”, IEEE special

section on industrial sensor networks with advanced

data management: design and security, 2015.

[48] Adnan Aijaz, “Opportunistic Routing in Diffusion-

Based Molecular Nanonetworks”, IEEE wireless

communications letters, Vol. 4, No. 3, June 2015.

[49] Dominique Zosso et al, “Fast Geodesic Active Fields

for Image Registration Based on Splitting and

Augmented Lagrangian Approaches”, IEEE

transactions on image processing, Vol. 23, No. 2, February 2014.

[50] Ramanan Subramanian et al, “Asymptotic

Throughput and Throughput-Delay Scaling in

Wireless Networks: The Impact of Error

Propagation”, IEEE transactions on wireless

communications, Vol. 13, No. 4, April 2014.

[51] Emilloc Ancilloti et al, “Reliable Data Delivery with

the IETF Routing Protocol for Low-Power and Lossy

Networks”, IEEE transactions on industrial

informatics, Vol. 10, No. 3, August 2014.

[52] Degan Zhang et al, “An Energy-Balanced Routing

Method Based on Forward-Aware Factor for Wireless Sensor Networks”, IEEE transactions on

industrial informatics, Vol. 10, No. 1, February 2014.

[53] Zhao Han et al, “A General Self-Organized Tree-

Based Energy-Balance Routing Protocol for Wireless

Sensor Network”, IEEE transactions on nuclear

science, Vol. 61, No. 2, April 2014.

[54] Mehdi Tarhani et al, “SEECH: Scalable Energy

Efficient Clustering Hierarchy Protocol in Wireless

Sensor Networks”, IEEE sensors journal, Vol. 14,

No. 11, November 2014.

[55] Duc Chinh Hoang et al, ” Real-Time Implementation of a Harmony Search Algorithm-Based Clustering

Protocol for Energy-Efficient Wireless Sensor

Networks”, IEEE transactions on industrial

informatics, Vol. 10, No. 1, February 2014.

[56] Alia Sabri and Khalil Al-Shqeera, “Hierarchical

Cluster-Based Routing Protocols for Wireless Sensor

Networks – A Survey”, IJCSI International Journal



7000

of Computer Science Issues, Vol. 11, Issue 1, No 2,

January 2014.

[57] Jae Yeol Ha et al, “EHRP: Enhanced Hierarchical

Routing Protocol for Zigbee Mesh Networks”, IEEE

communications letters, Vol. 11, No. 12, December

2007. [58] Stephanie Lindsey et al, “Data Gathering Algorithms

in Sensor Networks Using Energy Metrics”, IEEE

transactions on parallel and distributed systems, Vol.

13, No. 9, September 2002.

[59] Siva D. Murugananthan et al, “A Centralized

Energy-Efficient Routing Protocol for Wireless

Sensor Networks”, IEEE Radio Communications,

March 2005.

[60] Ossama Younis and Sonia Fahmy, “HEED: A

Hybrid, Energy-Efficient, Distributed Clustering

Approach for Ad Hoc Sensor Networks”, IEEE

transactions on mobile computing, Vol. 3, No. 4, October-December 2004.

[61] Chia-Hung et al, “A Comment on “HEED: A Hybrid,

Energy-Efficient, Distributed Clustering Approach

for Ad Hoc Sensor Networks”, IEEE transactions on

mobile computing, Vol. 5, No. 10, October 2006.

[62] Franck Le et al, “Interconnecting Routing Instances”,

IEEE/ACM transactions on networking, Vol. 22, No.

2, April 2014.

[63] Dali Wei et al, “An Energy-Efficient Clustering

Solution for Wireless Sensor Networks”, IEEE

transactions on wireless communications, Vol. 10, No. 11, November 2011.

[64] Mehmet C. Vuran and Ian F. Akyildiz, “XLP: A

Cross-Layer Protocol for Efficient Communication in

Wireless Sensor Networks”, IEEE transactions on

mobile computing, Vol. 9, No. 11, November 2010.

[65] Jianzhong Li et al, “Grouping-Enhanced Resilient

Probabilistic En-Route Filtering of Injected False

Data in WSNs”, IEEE transactions on parallel and

distributed systems, Vol. 23, No. 5, May 2012.

[66] Arati Manjeshwar et al, “An Analytical Model for

Information Retrieval in Wireless Sensor Networks

Using Enhanced APTEEN Protocol”, IEEE transactions on parallel and distributed systems, Vol.

13, No. 12, December 2002.

[67] Ciamu Tang et al, “Efficient Multi-Party Digital

Signature using Adaptive Secret Sharing for Low-

Power Devices in Wireless Networks”, IEEE

transactions on wireless communications, Vol. 8, No.

2, February 2009.

[68] Viet Anh et al, “One-Bit CSI Feedback Selection

Schemes for Energy-Efficient Multiuser and

Multirelay Systems”, IEEE transactions on wireless

communications, Vol. 12, No. 3, March 2013. [69] Theofanis P. Lambrou et al, “A Survey on Routing

Techniques supporting Mobility in Sensor

Networks”, Fifth International Conference on Mobile

Ad-hoc and Sensor Networks, 2009.

[70] Megha Jain et al, “Performance of Mobility Models

with different Routing Protocols by using Simulation

Tools for WSN: A Review”, International Journal of

Advanced Research in Computer and

Communication Engineering Vol. 4, Issue 1, January

2015.

[71] Poonam Rautela et al, “Zone based routing protocol

in WSNs for varying zone size”, International

Journal of Advanced Research in Computer

Engineering & Technology Volume 1, Issue 6, August 2012.

[72] Ravinder Kaur et al, “Routing Protocols for Mobile


Software and Web Sciences (IJSWS), 2013.

[73] M. Sasikumar and R. Anita, ” cluster based routing

protocol for wireless sensor networks”, International

Journal of Innovative Research in Advanced

Engineering (IJIRAE), Volume 1 Issue 9, October

2014.

[74] A. Balamurugan, “Energy Efficient Fitness Based

Routing Protocol in Wireless Sensor Networks”,

ICTACT journal on communication technology, March 2014, volume: 05, issue: 01.

[75] Eylem Ekici et al, “Mobility-Based Communication

in Wireless Sensor Networks”, IEEE

Communications Magazine, July 2006.

[76] Prerana Shrivastava and Dr. S. B. Pokle, “A Hybrid

Sink Positioning Technique for Data Gathering in


Engineering and Innovative Technology (IJEIT)

Volume 1, Issue 3, March 2012.

[77] Mohammed Abo-Zahhad et al, “Mobile Sink-Based

Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability

Period of Wireless Sensor Networks”, IEEE sensors

journal, Vol. 15, No. 8, August 2015.

[78] Ahmed Sobeih et al, “J-SIM: a simulation and

emulation environment for wireless sensor

networks”, IEEE Wireless Communications, August

2006.

[79] Madhumathi and D. Sivakumar, ” Enabling Energy

Efficient Sensory Data Collection Using Multiple

Mobile Sink”, China Communications, October

2014.

[80] Haibo Zhang and Hong Shen, “Energy-Efficient Beaconless Geographic Routing in Wireless Sensor


distributed systems, Vol. 21, No. 6, June 2010.

[81] Jinho Kim et al, “An ID/Locator Separation-Based

Mobility Management Architecture for WSNs”,

IEEE transactions on mobile computing, Vol. 13, No.

10, October 2014.

[82] Xinxin Liu et al, ” SinkTrail: A Proactive Data

Reporting Protocol for Wireless Sensor Networks”,

IEEE transactions on computers, Vol. 62, No. 1,

January 2013. [83] Jae-Wan Kim et al, “An Intelligent Agent-based

Routing Structure for Mobile Sinks in WSNs”, IEEE

Transactions on Consumer Electronics, Vol. 56, No.

4, November 2010.

[84] Mohammed Abo-Zahhad et al, “Mobile Sink-Based

Adaptive Immune Energy-Efficient Clustering

Protocol for Improving the Lifetime and Stability



7001

Period of Wireless Sensor Networks”, IEEE sensors

journal, Vol. 15, No. 8, August 2015.

[85] Miao Zhao et al, “A Framework of Joint Mobile

Energy Replenishment and Data Gathering in

Wireless Rechargeable Sensor Networks”, IEEE

transactions on mobile computing, Vol. 13, No. 12, December 2014.

[86] Ying-Hong Wang et al, “HMRP: Hierarchy-Based

Multipath Routing Protocol for Wireless Sensor

Networks”, Tamkang Journal of Science and

Engineering, Vol. 9, No 3, pp. 255-264, 2006.

[87] Yujun Zhang et al, “TOHIP: A topology-hiding

multipath routing protocol in mobile ad hoc

networks”, Elsevier, 2-14, pp: 109-122.

[88] Natarajan Meghanathan, “A node-disjoint multi-path

routing protocol based on location prediction for

mobile ad hoc networks”, International Journal of

Engineering, Science and Technology Vol. 2, No. 5, 2010, pp. 66-80.

[89] A. Monisha and K. Vijayalakshmi, “A reliable node-

disjoint multipath routing protocol for MANET”,

International Journal of Computational Engineering

Research, Vol, 03, Issue, 4, 2013.

[90] Priya Gopi, “Energy-Aware Node Disjoint Multipath

Routing Protocol for Wireless Sensor Networks”,

International Journal of Computer Trends and

Technology (IJCTT) – volume 13 number 3 – Jul

2014.

[91] Jay Kumar Jain et al, “Performance Analysis of Node-Disjoint Multipath Routing for Mobile Ad-hoc

Networks based on QoS”, International Journal of

Computer Science and Information Technologies,

Vol. 3 (5), 2012, 5000 – 5004.

[92] M. Bheemalingaiah et al, “Energy aware node

disjoints multipath routing in mobile ad hoc

network”, Journal of Theoretical and Applied

Information Technology, 2009.

[93] Rajendra Kumar Gupta, “Node Disjoint Minimum

Interference Multipath (ND-MIM) Routing Protocol

for Mobile Ad hoc Networks”, International Journal

of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 3, March

2012.

[94] Jayshree Tajne et al, “Multipath Node-Disjoint

Routing Protocol to Minimize End to End Delay and

Routing Overhead for MANETs”, International

Journal of Engineering Research and

Applications (IJERA), Vol. 3, Issue 4, Jul-Aug 2013, pp.

1691-1698.

[95] Hakki Bagci et al, “A Distributed Fault-Tolerant

Topology Control Algorithm for Heterogeneous

Wireless Sensor Networks”, IEEE transactions on parallel and distributed systems, Vol. 26, No. 4, April

2015.

[96] Shancang Li et al, “Adaptive and Secure Load-

Balancing Routing Protocol for Service-Oriented

Wireless Sensor Networks”, IEEE systems journal,

Vol. 8, No. 3, September 2014.

[97] Zhibo Wang et al, “Achieving k-Barrier Coverage in

Hybrid Directional Sensor Networks”, IEEE

transactions on mobile computing, Vol. 13, No. 7,

July 2014.

[98] Osameh M. Al-Kofahi et al, “Scalable Redundancy

for Sensors-to-Sink Communication”, IEEE/ACM

transactions on networking, Vol. 21, No. 6,

December 2013. [99] Ameer A. Abbasi et al, “Recovering from a Node

Failure in Wireless Sensor-Actor Networks with

Minimal Topology Changes”, IEEE transactions on

vehicular technology, Vol. 62, No. 1, January 2013.

[100] Hongli Xu et al, “Bandwidth-Power Aware

Cooperative Multipath Routing for Wireless

Multimedia Sensor Networks”, IEEE transactions on

wireless communications, Vol. 11, No. 4, April 2012.

[101] Ying Lin et al, “An Ant Colony Optimization

Approach for Maximizing the Lifetime of

Heterogeneous Wireless Sensor Networks”, IEEE

transactions on systems, man, and cybernetics, Vol. 42, No. 3, May 2012.

[102] Marwan Al-Jemeli et al, “An Energy Efficient Cross-

Layer Network Operation Model for IEEE 802. 15.

4-Based Mobile Wireless Sensor Networks”, IEEE

sensors journal, Vol. 15, No. 2, February 2015.

[103] Miltiades C. Filippou et al, “A Comparative

Performance Analysis of Interweave and Underlay

Multi-Antenna Cognitive Radio Networks”, IEEE

transactions on wireless communications, Vol. 14,

No. 5, May 2015.

[104] Junfeng Wang et al, ” PWDGR: Pair-Wise Directional Geographical Routing Based on Wireless

Sensor Network”, IEEE internet of things journal,

Vol. 2, No. 1, February 2015.

[105] Mohammad J. Abdel-Rahman et al, “QoS-aware

Parallel Sensing/Probing Architecture and Adaptive

Cross-layer Protocol Design for Opportunistic

Networks”, IEEE transactions on vehicular

technology, 2015.

[106] Peng-Yong Kong et al, “Wireless Neighborhood

Area Networks with QoS Support for Demand

Response in Smart Grid”, IEEE transactions on smart

grid, 2015. [107] Sudip Misra et al, “A Cooperative Bargaining

Solution for Priority-Based Data-Rate Tuning in a

Wireless Body Area Network”, IEEE transactions on

wireless communications, Vol. 14, No. 5, May 2015.

[108] Naimah Yaakob et al, “By-Passing Infected Areas in

Wireless Sensor Networks Using BPR”, IEEE

transactions on computers, Vol. 64, No. 6, June

2015.

[109] Jiao Zhang et al, “Dynamic Routing for Data

Integrity and Delay Differentiated Services in

Wireless Sensor Networks”, IEEE transactions on mobile computing, Vol. 14, No. 2, February 2015.

[110] R. Velmani and B. Kaarthick, “An Efficient Cluster-

Tree Based Data Collection Scheme for Large

Mobile Wireless Sensor Networks”, IEEE sensors

journal, Vol. 15, No. 4, April 2015.

[111] Haijun Zhang et al, “Interference-Limited Resource

Optimization in Cognitive Femtocells with Fairness



7002

and Imperfect Spectrum Sensing”, IEEE

Transactions on Vehicular Technology, 2015.

[112] Xing zhang et al, “Energy-Efficient Multimedia

Transmissions through Base Station Cooperation

over Heterogeneous Cellular Networks Exploiting

User Behavior”, IEEE Wireless Communications, August 2014.

[113] Long Cheng et al, “QoS Aware Geographic

Opportunistic Routing in Wireless Sensor


distributed systems, Vol. 25, No. 7, July 2014.

[114] W. Heinzelman, J. Kulik, and H Balakrishnan,

“Adaptive protocols for information dissemination in

wireless sensor networks”, in: Proceedings of the 5th

Annual ACM/IEEE international conference on Mobile Computing and Networking(MobiCom’99), Seattle WA, August 1999.

[115] D. Bramgomslu and D. Estrin, “Roumor Routing Algorithm For Sensor Networks, ” Proc. First workshop Sensor Networks and Applications (WSNA’02), Oct, 2002.

[116] S. Shakkottai, ”Asymptotics of Query strategies over

a sensor Network, ” Proceedings of IEEE INFOCOM, Mar-2004

[117] Carlos de Morais corderio and Dharma Prakash

Agrawal, “Ad Hoc and Sensor Network Theory and

Applications” 2nd edition-book.

[118] A. manjeshwar and D. Agrawal, ”TEEN: A Protocol

for Enhanced Efficiency in Wireless Sensor Networks, ” Proceedings of the 1st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing,

April 2001.

[119] A. Manjeshwar and D. Agrawal, “APTEEN: A

Hybrid Protocol for Efficient Routing and

Comprehensive Information Retrieval in Wireless

Sensor Networks”, Proceedings f 2nd International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing,

April 2002

[120] S. Lindsey, C. S. Raghavendra, and K. Sivalingam, “Data gathering in sensor networks using the energy

delay metric”, in: Proceedings of the IPDPS Workshop on Issues in Wireless Networks and Mobile Computing, San Francisco, CA. April 2001.

[121] M. Shanmukhi, OBV. Ramanaiah, ” Cluster-based

Comb-Needle Model for Energy-efficient Data

Aggregation in Wireless Sensor Networks”, IEEE International Conference AIMOC-2015 12th-14th

February 2015.

Authors detail

Dr. O. B. V. Ramanaiah, Professor, CSE

Department, JNTU College of Engineering,

Hyderabad, India He received Ph.D. in Computer

Science from University of Hyderabad in 2005,

M.Tech(CS) from JNTU, India, and B. Tech(CSE)

from SVUCE, Tirupathi, India. His Total Teaching

Experience is 22 Years with Total Research

Experience of 13 Years. He has published research

papers in national & International Journals and

Conferences. He visited USA for paper presentation

in an IEEE Conference, ITCC 04, held during April

5-7, 2004. Currently he is providing Research

Guidance to 8 students and organized many

Refresher Courses. His research interests include

Computer Networks Distributed Systems, Mobile

Computing, Ad hoc & Sensor Networks.

Ms. M. Shanmukhi is currently pursuing her Ph.D

degree in CSE at JNTU, Hyderabad, India.

Received M.Tech (CS), and B.Tech (CSE) from

JNTU, Hyderabad, India. She has more than 11

years of teaching experience, currently working as

an Associate professor in the department of CSE,

Malla Reddy college of Engineering, Affiliated to

JNTU, Hyderabad, India. She has published

research papers in national & International

Conferences. Her main research interests include

wireless Adhoc & Sensor networks, Multimedia

communications, TCP/IP Networks and Protocols.

Documents

A Survey on Energy Efficient Data Aggregation Protocols ... · PDF file... Span, TBF, BVGF, GeRaF Data-centric Protocols SPIN, Directed Diffusion, Rumor Routing, ... False Alarm (FA)