A Survey on Routing Protocols in Wireless Sensor Networks.pdf

8/9/2019 A Survey on Routing Protocols in Wireless Sensor Networks.pdf

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A Survey on Routing Protocols in Wireless Sensor

Networks

Mallanagouda PatilDepartment of Computer Science and Engineering

Wireless Information Systems Research Laboratory

Reva Institute of Technology and Management

Bangalore-560 064, India

Email: [email protected]

Rajashekhar C. BiradarDepartment of Electronics and Communication Engineering

Wireless Information Systems Research Laboratory

Reva Institute of Technology and Management

Bangalore-560 064, India

Email: [email protected]

AbstractThis paper presents a survey of state-of-the-art rout-ing techniques in Wireless Sensor Networks (WSNs). Comparedwith traditional wireless networks, WSNs are characterized withdenser levels of node deployment, higher unreliability of sensornodes and severe power, computation and memory constraints.Various design challenges such as energy efficiency, data deliverymodels, quality of service, overheads etc., for routing protocolsin WSNs are highlighted. We addressed most of the proposedrouting methods along with scheme designs, benefits and resultanalysis wherever possible. The routing protocols discussed areclassified into seven categories such as Data centric routing,Hierarchical routing, Location based routing, Negotiation basedrouting, Multipath based routing, Quality of Service (QoS)routing and Mobility based routing. This paper also compares therouting protocols against parameters such as power consumption,scalability, mobility, optimal routing and data aggregation. Thepaper concludes with possible open research issues in WSNs.

Keywords: WSNs, Energy Efficiency, QoS, Cluster;

I. INTRODUCTION

With the popularity of laptops, cell phones, global posi-

tioning system (GPS) devices, radio frequency infrared devices(RFID) and intelligent electronics, computing devices have be-

come cheaper, more mobile, distributed and pervasive in daily

life. Wireless Sensor Network (WSN) consists of large number

of small low-cost, low-power and multifunctional sensor nodes

where each node has a processing capability, multiple types of

memory, radio frequency (RF) transceiver and a power source.

In addition, the nodes accommodate sensors and actuators [1].

In many WSN applications, the deployment of sensor nodes

is performed in an adhoc fashion without careful preplanning

and engineering. After the initial deployment, sensor nodes

communicate wirelessly and self organize into an appropriate

network infrastructure often with multihop connections among

sensor nodes. Sensor nodes are typically battery-powered andshould operate without attendance for a relatively longer period

of time. In most cases, it is very difficult and even impossible

to change or recharge batteries for the sensor nodes. The design

of routing protocols in WSNs is challenging because of several

network constraints with an emphasis on energy effeciency.

A. Routing Challenges in Sensor Networks

Some of the routing challenges in WSN are as follows.

Energy Consumption : As sensor nodes in WSN have lim-

ited battery power, it becomes challenging to perform computa-

tion and transmission while optimizing energy consumption[1].

In fact the transmission of one bit of data consumes more

energy than processing the same bit of data. Sensor node life

time strongly depends on its battery life.

Node Deployment : Sensor nodes are usually denselydeployed in the field of interest depending on application

thus influencing the performance of a routing protocol. The

deployment can be either deterministic or self-organizing.

In deterministic case, the sensor nodes are manually placed

and sensed data is routed through determined paths. In self-

organizing systems, sensor nodes are scattered randomly cre-

ating a topology in an adhoc manner[2].

Data Delivery Models : Data delivery models can be time

driven, data driven, query driven and hybrid (combination of

delivery models) depending on the application of sensor nodes

and time criticality of data reporting. These data delivery mod-

els highly influence the design of routing protocols especially

with regard to reducing energy consumption[3],[4].

Node Capability : Depending on the application, a sensor

node can have different role or capability such as relaying,

sensing and aggregation since engaging all these functions on

the same node would drain the energy of that node more

quickly. Different capabilities of sensor nodes raise multi-

ple issues related to data routing and makes routing more

challenging[5],[6],[7].

Network Dynamics : Most of the network architectures

assume that sensor nodes are static but the mobility of base

stations and sensor nodes is necessary in some applications

[8]. Routing packets in such dynamic architectures becomes

challenging in addition to minimizing energy consumption andbandwidth utilization.

Data Aggregation : Since sensor nodes generate redundant

data, cluster heads or base stations may receive similar packets

from multiple nodes and these packets need to be aggregated

before being forwarded to the base station. Signal processing

methods can also be used for data aggregation[9].

Other routing challenges in WSNs are scalability, coverage

area[10], transmission media[11], fault tolerance and QoS[12].

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I I . TAXONOMY OFROUTINGP ROTOCOLS

We present taxonomy of routing potocols for WSNs based

on various classification criteria such as data centric, hier-

archical, location based, negotiation based, multipath based,

quality of service and mobility based as shown in figure

1. The objective of taxonomy is twofold: (1) to provide a

framework Wireless Sensor Network in which routing and

data dissemination protocols for WSNs can be examined and

compared; and (2) to gain new insights into the routing anddata dissemination protocols and thereby suggest avenues for

future research.

Data Centric Directed Diffusion

Hierachical LEACH, TEEN and APTEEN

Location Based GAF and GEAR

Negotiation SPIN

MultiPath Disjoint Path Routing

and Rumor Routing

Mobility Joint Mobility

QOS Based Sequential Assignment Routing

and Routing

Fig. 1. Taxonomy of Routing Protocols in WSN

A. Data Centric Routing

Lack of global identification along with random deployment

of sensor nodes makes it hard to select a specific set of sensor

nodes to be queried. Since data is usually transmitted with

significant redundancy. This is very inefficient in terms of

energy consumption, routing protocols that are able to select

a set of sensor nodes and utilize data aggregation during the

relaying of data have been considered. This consideration has

led to data-centric routing, a new communication paradigm

where attribute based naming is necessary to specify the

properties of data[13].

Directed Diffusion :Being an important data-centric routing

protocol in sensor networks, Directed Diffusion [14] suggeststhe use of attribute-value pairs for the desired data and queries

sensors in an on demand basis.

Proposed Scheme : In Directed Diffusion, if a sensor wants

to receive data, it sends interests for named data. When data is

sent by a source sensor, the data can be cached or transformed

by intermediate sensor, which in turn may initiate interests

based on the data that were previously cached. In addition,

the data sent by the source sensor may be aggregated by

intermediate sensors before being forwarded to destination.

More importantly, interests, data dissemination and data ag-

gregation occur in directed diffusion in a localized manner

via exchanging messages between neighboring nodes. Directed

Diffusion has several key elements: data naming, interests,

gradients, data propagation and reinforcement. The sink peri-

odically broadcasts an interest for each active task specifying

a low data rate. To ensure reliable interest transmission, an

interest has a time-stamp that allows the sink to refresh the

interest by resending it with a monotonically increasing time-stamp value. When a sensor receives an interest, it may resend

it to some subset of its neighbors or even rebroadcast it to all

of its neighbors (in case it does not have information about

the sensors that satisfy the interest). This allows interests to

be diffused throughout the network. A response to the interest

is also named. A sensor will consult its interest cache to decide

to which neighbors it has to unicast this event description

using the highest data rate over all its outgoing gradients.

Upon receiving a data message, a sensor may drop it if it

does not find a matching interest in its cache. Otherwise, it

checks the data cache, which contains the data messages seen

recently, corresponding to the matching interest entry. As a

result, this data message can be cached and resent to theneighbors, if it is not already in the data cache. Otherwise,

this data message is simply dropped. If the data rate specified

in all gradients is higher than that of incoming events, the

receiving sensor will simply send the received data message to

the corresponding neighbors. Otherwise, the receiving sensor

may down convert to the specified data rate for the neighbors

with a lower requested data rate. Figure 2 summarizes the steps

in Directed Diffusion.

Fig. 2. Directed Diffusion Protocol Description (a) Interest propagation (b)Initial gradients setup (c) Data delivery reinforced.

Benefits/Demerits of the Scheme : All communication isneighbor-to-neighbor and on demand with no need for node

addressing mechanism. Caching is a big advantage in terms of

energy efficiency and delay. Applications that require contin-

uous data delivery to the sink will not work efficiently with

such a query-driven on demand data model.

B. Hierarchical Routing

In hierarchical architecture, sensor nodes are organized into

clusters, where a node with lower energy can be used to

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perform the sensing task and send the sensed data to its cluster

head at short distance, while a node with higher energy can

be selected as a cluster head to aggregate the data from its

members and forward it to the sink [15]. This process can not

only reduce the energy consumption, but also balance traffic

load and improve the scalability[16].

Threshold Sensitive Energy Efficient Sensor Network Proto-

col (TEEN ) : A sensing application can be designed in a way

where the sensors either sense and transmit data periodically tothe sink (proactive) or react immediately to any sudden change

in the value of sensed attribute (reactive). For time-critical ap-

plications, a reactive network is more suitable than a proactive

network. In order to trade-off between energy efficiency, data

accuracy and response time dynamically, a communication

protocol, named as TEEN has been proposed[17].

Proposed Scheme : TEEN is a clustering protocol that

targets a reactive network and enables cluster heads to impose

constraint on when the sensor nodes should report their sensed

data. Each cluster head broadcasts to its members a value,

called hard threshold (HT), for the sensed attribute beyond

which a sensor should turn its transmitter on to report its sensed

data to cluster head. Cluster head also broadcasts another value,called soft threshold (ST), which indicates a small change

in the value of the sensed attribute, making sensor turn on

its transmitter and send data to the cluster head. TEEN is

a hierarchical protocol designed to be responsive to sudden

changes in the sensed attributes such as temperature. TEEN

pursues a hierarchical approach along with the use of a data-

centric mechanism. The Adaptive Threshold sensitive Energy

Efficient sensor Network protocol (APTEEN) is an extension

to TEEN and targets both reactive and proactive networks[18].

Result Analysis : Experimental results have shown that

TEEN and APTEEN outperform LEACH (Low Energy Adap-

tive Clustering Hierarchy). APTEENs performance is between

LEACH and TEEN in terms of energy dissipation and network

lifetime. TEEN gives the best performance since it decreases

the number of transmissions.

Benefits/Demerits of the Scheme : Both hard and soft

threshold values can be adjusted in order control the number

of transmissions. However, TEEN is not a good choice for

periodic data reporting applications. The main drawbacks of

TEEN and APTEEN are the overhead and complexity of

forming clusters in multiple levels, implementing threshold-

based functions and dealing with attribute-based naming of

queries.

C. Location Based Routing

In most cases, location information is needed to calculate

the distance between two particular nodes so that energy

consumption can be estimated. Geographic Adaptive Fidelity

(GAF) protocol [19] is a location based protocol although

proposed for Mobile Adhoc Networks (MANETs), it favors

energy conservation and thus can be used for WSNs.

Geographic Adaptive Fidelity (GAF) : If the region to be

sensed is known, using the location of sensors, query can

be diffused only to that region thus reducing the number

of transmissions significantly. It is possible to locate nodes

through satellites or GPS (Global Positioning System) on the

basis of the signal strength passed between neighbor nodes.

The common approach for energy saving is to use sleep modes

in nodes expecting no activity in a period of time. This is the

main idea behind GAF.

Proposed Scheme : The design of GAF is motivated by an

energy model that focuses on energy consumption due to the

reception and transmission of packets as well as idle time[19].GAF is based on the mechanism of turning off unnecessary

sensors while keeping a constant level of routing fidelity. GAF

divides sensor field into grid squares and every sensor uses its

location information to associate itself with a particular grid.

Size of grid square is chosen in a way such that sensors within

the same grid are equivalent with regard to routing and that

sensors in adjacent grids can communicate with each other.

Equivalent sensors can coordinate with each other to determine

an energyefficient schedule of their activities, which specifies

when and for how long the sensors stay awake or sleep. As

shown in figure 3 the state transition diagram of GAF has three

states, namely discovery, active and sleeping.

Sleeping

Recieve Discovery msg

from high rank nodes Active

Discovery

After Ts

After Td

After Ta

from high rank nodesRecieve msg

Fig. 3. State Transition Diagram for GAF

GAF maintains only one node called an active node with

its radio turned on per grid square and this active node is

responsible for relaying traffic on behalf of its grid square.

This responsibility is rotated among all the nodes. Whenever

a node changes state to discovery or active state, it sends a

broadcast message containing node ID, grid ID and the value of

ranking function. If a node in discovery or active state receivesa message from a node in the same grid and a higher value of

the grid function, it is allowed to change its state to sleep and

turn its radio transceiver off for time Ts. Usually the ranking

function selects nodes with longest expected life time as

the active nodes. In sleeping state, sensor turns off its radio

for energy savings. In the discovery state, a sensor exchanges

discovery messages to learn about other sensors in the same

grid. Even in the active state, a sensor periodically broadcasts

its discovery message to inform equivalent sensors about its

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state. GAF aims to maximize the network lifetime by reaching

a state where each grid has only one active sensor based on

sensor ranking rules.

Benefits/Demerits of the Scheme : The amount of energy

saved by using GAF protocol is considerable due to large

number of sleeping nodes. The drawback is that the GAF uses

extra hardware for finding the location of sensor nodes.

D. Negotiation Based Routing

These protocols use high level data descriptors called meta-

data in order to avoid redundant data transmissions through

negotiation. Communication decisions are also taken based on

the resources available.

SPIN (Sensor Protocol for Information via Negotiation) :

For applications like intruder detection, disseminating individ-

ual sensor observations to all sensors in a network should

be performed as energy efficient as possible. In light of

this,a family of adaptive protocols named SPIN has been

proposed. The SPIN protocols were designed to overcome the

problems of flooding. The SPIN protocols are resource aware

and resource adaptive. They can make informed decisions for

efficient use of their own resources[20].Proposed Scheme : SPIN protocols are based on two

key mechanisms: Negotiation and Resource Adaptation. SPIN

enables the sensors to negotiate with each other by using meta-

data before any data transfer to avoid redundant information in

the network. Each sensor is aware of its resource consumption

with the help of its own resource manager that is probed by the

application before any data processing or transmission. This

helps the sensors monitor and adapt to any change in their

own energy resources. There are three messages defined in

SPIN to exchange data between the nodes. These are: ADV

message to allow a sensor to advertise a particular meta-data,

REQ message to request the specific data and DATA message

that carries the actual data.

Benefits/Demerits of the Scheme : Advantage of SPIN

protocol is that each node only knows its single-hop neighbors

thus localizing the topological changes in the network. The

drawback is that the data advertisement mechanism cannot

guarantee the delivery of data if the nodes that are interested

in the data are far away and the nodes between source and

destination are not interested in that data. Meta-data introduce

additional costs for storage, retrieval and management.

E. Multipath Based Routing

These protocols use multiple paths instead of single path

to enhance network performance by providing fault tolerance.These alternate paths are kept alive by sending periodic mes-

sages.

Disjoint Path Routing : In multipath routing, each source

sensor node finds the first k shortest paths to the sink and

divides its load evenly among these paths. Multipath protocols

help find a small number of alternate paths that have no

sensor in common with each other and with the primary path.

These protocols are said to be sensor-disjoint multipath routing

protocols[21].

Proposed Scheme : In sensor-disjoint path routing, the

primary path is best available whereas the alternative paths

are less desirable as they have longer latency. Being disjoint

makes those alternate paths independent of the primary path.

Thus, if a failure occurs on the primary path, it remains local

and does not affect any of those alternate paths. The sink

sends out a primary-path reinforcement to its best neighbor.

This reinforcement process repeats until the construction of

the primary path is done. After that, the sink iterates the sameoperation for its next most preferred neighbor by sending out to

it an alternate-path reinforcement. If each sensor accepts only

the first reinforcement, those alternate paths are guaranteed to

be disjoint with each other and with the primary path.

Benefits/Demerits of the Scheme : Although maintaining

alternate paths introduces some overhead and consumes more

energy, multipath routing is an effective technique to improve

robustness in the face of path failures caused by frequent

topological changes. More specifically, multipath routing helps

recover from sensor and link failures and provide necessary

resilience to the network at the cost of excessive redundancy

and traffic generation.

F. QoS Based Routing

In addition to minimizing energy consumption, it is essen-

tial to consider QoS requirements in terms of delay, reliability

and fault tolerance for routing in WSNs. Both fault tolerance

and reliability require the deployment of more than necessary

sensors so that the network can continue to function properly

and deliver accurate sensed data to the sink despite some sensor

failures [22]. Sequential Assignment Routing (SAR) is one of

the first routing protocols for WSNs that introduces the notion

of QoS in the routing decisions.

Sequential Assignment Routing (SAR) : Routing decision in

SAR depends on three factors: energy resources, QoS on each

path and the priority level of each packet. To avoid single route

failure, a multi-path approach and localized path restoration

schemes are used.

Proposed Scheme : To create multiple paths from a source

node, a tree rooted at the source node to the destination nodes

is built covering each node. SAR is table-driven multipath

protocol that aims to achieve energy efficiency and fault toler-

ance. SAR calculates a weighted QoS metric as the product of

additive QoS metric and weight coefficient associated with the

priority level of the packet. The objective of SAR algorithm is

to minimize the average weighted QoS metric throughout the

lifetime of the network. As a preventive measure, a periodicre-computation of paths is triggered by the base-station to

account for any changes in the topology. Failure recovery is

done by enforcing routing table consistency between upstream

and downstream nodes on each path.

Benefits/Demerits of the Scheme : Simulation results show

that SAR offers less power consumption than the minimum-

energy metric algorithm that does not consider the packet prior-

ity. Although, SAR ensures fault-tolerance and easy recovery,

the protocol suffers from the overhead of maintaining the tables

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and states at each node especially when the number of nodes

is huge.

G. Mobility Based Routing

Some sensor applications require mobile nodes to accom-

plish a sensing task. Mobility brings new challenges to routing

and data dissemination in WSNs and increases the complexity

of implementation.Joint Mobility and Routing Protocol : A network with a

static sink suffers from a severe problem, called energy sink-

hole problem where the sensors located around the static sink

are heavily used for forwarding data to sink. As a result, those

heavily loaded sensors close to the sink deplete their battery

power more quickly, thus disconnecting from the network.

This problem exists even when the static sink is located at

its optimum position corresponding to the center of the sensor

field. To address this problem, a mobile sink for gathering

sensed data from source sensors has been suggested[23].

Proposed Scheme : In Joint Mobility and Routing Protocol,

the sensors surrounding the sink change over time, giving the

chance to all sensors in the network to act as data relays tothe mobile sink and thus balancing the load. In particular,

the optimum mobility strategy of the sink is a symmetric

strategy in which the trajectory of the sink is the periphery

of the network. This result was shown by comparing mobility

trajectories on concentric circles of different radii and it was

proved that the maximum average load of data routing is

achieved at the network center. Therefore, the trajectory with

a radius equal to the radius of the sensor eld maximizes

the distance from the sink to the center of the network that

represents the hot spot. Regardless of the shape of a sensor

eld, it is always true that the sensors located on the network

periphery are almost not used for forwarding sensed data to

the sink on behalf of other sensors and thus have a longer

lifetime than all other sensors in the network. Therefore, an

efcient routing strategy should exploit the available energy

of those sensors close to the border of the network in order

to balance the data dissemination load on all the sensors.

Based on the sink mobility strategy and the routing strategy

discussed earlier, an energy-efficient heuristic for joint routing

and mobility is to have the sink moving on a circle of radius

Rm R, where R stands for the radius of the sensor eld, while

data routing depends on the location of the source sensors.Note

that the sensor field is divided into two regions: the inner circle

and the annulus between the periphery of the network and

the trajectory of the sink represented by a circle. The sensorswithin the inner circle use the shortest path routing to transmit

their sensed data to the sink, whereas the sensors in the annulus

send their data to the sink using two steps. First, a sensor

uses round routing around the center of the network O until

the segment OB is reached, where B is the current position

of the mobile sink. Then, the data is sent to the sink using

a shortest path. This joint heuristic leads to lower network

load by reducing the distance between the mobile sink and the

sensors that follow the shortest path.

Benefits/Demerits of the Scheme : Although many perfor-

mance aspects of WSNs may benefit from mobility, the lifetime

issue seems to have attracted the majority of attention and

contributions. The traffic load within a WSN is highly unbal-

anced among nodes that have different distances from the sink.

Whereas no routing strategy may alleviate such an imbalance,

actively moving certain network entities may further balance

the load and hence improve the lifetime. However the mobility

in WSN leads to an added complexity compared to static WSN.

III. COMPARISON OFP ROTOCOLS

The figure 4 shows the similarities between the protocols

based on the classification criteria used in the taxonomy.

GEAR

Limited

Limited

NO

NO

Limited

NO

NO

NO

NO

NO

NO

SAR

Maximum

Limited

NO

NO

NO

NO

NO

YES

NO

YES

NO

SPIN

Limited

Limited

NO

NO

Possible

YES

YES

NO

NO

NO

NO

Limited

Good

NO

Limited

NO

NO

NO

YES

YES

NO

LEACH

Maximum

Good

NO

NO

NO

Fixed Sink

YES

YES

NO

NO

NO

Directed

Diffusion

Limited

Limited

NO

YES

Limited

YES

YES

NO

YES

NO

NO

GAF

NO

Energy Consumption

Scalability

Location Awareness

Optimal Routing

Mobility

Commercial

Military

MonitoringEnvironment

Health

Data Aggregation

MultiPath

Fig. 4. Comparison of Routing Protocols in WSNs

IV. CONCLUSION ANDO PE NI SSUES

In recent years, routing in WSN has gained tremendous

attention leading to unique challenges and design issues when

compared to routing in traditional wired networks. In this

paper, we have discussed recent research activities on routing

in WSNs and classified the approaches to routing in seven main

categories. In case of Data centric routing, naming rules such

as attribute-value pairs will not work for complex queries that

are application dependent. The building of standard efficient

naming schemes is one of the open issues for future research

in this category. In case of Hierarchical routing, the nodes are

grouped together to form clusters. Cluster heads are respon-sible for data aggregation and relay of messages to the sink.

The design issues for such protocols are how to form clusters

and select cluster heads so that energy consumption in the

communication of redundant messages as well as aggregation

is reduced. The factors affecting cluster formation and cluster

head communication are open future research issues in this

category. In case of Location based routing protocols, energy

efficient and intelligent utilization of location information is

an open research issue. QoS based routing has its own quality

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requirements when it comes to real time applications like target

tracking in battle fields. Handling the QoS requirements in

energy efficient way is one of the open research issues in

QoS based routing. Many of the current routing protocols in

WSN assume that nodes and sink are static. However, there

are situations like battle field environments where sinks as

well as the sensor nodes need to be mobile. New routing

algorithms are required to accommodate mobility and dynamic

topology changes in energy constrained environment of WSNs.Another possible future research area for routing protocols is

the integration of internet with WSNs so that the data sensed

in one part of the world can be sent to the server located in

another part of the world for further analysis.

ACKNOWLEDGMENT

Authors would like to thank Visvesvaraya Technological

University (VTU), Karnataka, INDIA, for sponsoring the

part of the project under VTU Research Scheme, grant no.

VTU/Aca/2011-12/A-9/753, Dated: 5th May 2012.

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A Survey on Routing Protocols in Wireless Sensor Networks.pdf