A SURVEY OF SENSOR NETWORK AND RELATED ROUTING

PROTOCOLS

O. P. Vyas, M. K. Tiwari

S. O. S. in Computer Science, Pt. Ravishankar Shukla University, Raipur, India

Chandresh Pratap Singh

Department of Computer Science/IT, Dr. C. V. Raman University, Kargi Road – Kota, Bilaspur, India

Keywords: Routing Protocols.

Abstract: Recent advances in wireless sensor networks have led to many new protocols specifically designed for

sensor networks where energy awareness is an essential consideration. Most of the attention, however, has

been given to the routing protocols since they might differ depending on the application and network

architecture. Tiny sensor nodes create sensor network. These nodes are severely constrained by energy,

storage capacity and computing power. The prominent task of this network is to design proficient routing

protocols for to make the node’s life last longer. In this paper, we first analyze the requirements of sensor

networks and its architecture. Then, we enlighten the existing routing protocols for sensor networks and

present a critical analysis of these protocols. The paper concludes with open research issues. At the end of

this paper, we compare and contrast these protocols.

1 INTRODUCTION

Recent advances in micro-electro-mechanical

systems (MEMS) and low power and highly

integrated digital electronics have led to the

development of micro sensors. Such sensors are

generally equipped with data processing and

communication capabilities. The sensing circuitry

measures ambient conditions related to the

environment surrounding the sensor and transform

them into an electric signal. Processing such a signal

reveals some properties about objects located and/or

events happening in the vicinity of the sensor. The

sensor sends such collected data; usually via radio

transmitter (1), to a command center (sink) either

directly or through a data concentration center (a

gateway). A natural architecture for such

collaborative distributed sensors is a network with

wireless links that can be formed among the sensors

in an ad hoc manner.

Networking unattended sensor nodes are expected to

have significant impact on the efficiency of many

military and civil applications such as combat field

surveillance, security and disaster management.

These systems process data gathered from multiple

sensors to monitor events in an area of interest. For

example, in a disaster management setup, a large

number of sensors can be dropped by a helicopter.

Networking these sensors can assist rescue

operations by locating survivors, identifying risky

areas and making the rescue crew more aware of the

overall situation. Such application of sensor

networks not only can increase the efficiency of

rescue operations but also ensure the safety of the

rescue crew.

On the military side, applications of sensor networks

are numerous. For example, the use of networked set

of sensors can limit the need for personnel

involvement in the usually dangerous

reconnaissance missions. In addition, sensor

networks can enable a more civic use of landmines

by making them remotely controllable and target-

specific in order to prevent harming civilians and

animals. Security applications of sensor networks

include intrusion detection and criminal hunting.

However, sensor nodes are constrained in energy

supply and bandwidth. These challenges necessitate

energy-awareness at all layers of networking

protocol stack. The issues related to physical and

link layers are generally common for all kind of

215

P. Vyas O., K. Tiwari M. and Pratap Singh C. (2008).

A SURVEY OF SENSOR NETWORK AND RELATED ROUTING PROTOCOLS.

In Proceedings of the Third International Conference on Software and Data Technologies - PL/DPS/KE, pages 215-220

DOI: 10.5220/0001871802150220

 SciTePress

sensor applications. At the network layer, the main

aim is to find ways for energy efficient route setup

and reliable relaying of data from the sensor nodes

to the sink so that the lifetime of the network is

maximized. Routing in sensor networks is very

challenging due to several characteristics that

distinguish them from contemporary communication

and wireless ad-hoc networks. First of all, it is not

possible to build a global addressing scheme for the

deployment of sheer number of sensor nodes.

Second, in contrary to typical communication

networks almost all applications of sensor networks

require the flow of sensed data from multiple

regions (sources) to a particular sink. Third,

generated data traffic has significant redundancy in

it since multiple sensors may generate same data

within the vicinity of a phenomenon. Such

redundancy needs to be exploited by the routing

protocols to improve energy and bandwidth

utilization. Fourth, sensor nodes are tightly

constrained in terms of transmission power, on-

board energy, processing capacity and storage and

thus require careful resource management. Due to

such differences, many new algorithms have been

proposed for the problem of routing data in sensor

networks. These routing mechanisms have

considered the characteristics of sensor nodes along

with the application and architecture requirements.

Almost all of the routing protocols can be classified

as data-centric, hierarchical or location-based. Data-

centric protocols are query-based and depend on the

naming of desired data, which helps in eliminating

many redundant transmissions. Hierarchical

protocols aim at clustering the nodes so that cluster

heads can do some aggregation and reduction of data

in order to save energy. Location-based protocols

utilize the position information to relay the data to

the desired regions rather than the whole network. In

this paper, we will explore the routing protocols for

sensor networks developed in recent years. Each

routing protocol is discussed under the proper

category. Our aim is to help better understanding of

the current routing protocols for wireless sensor

networks and point out open issues that can be

subject to further research. The paper is organized as

follows. In the section 2, we will briefly summarize

the system architecture design issues for sensor

networks and their implications on data routing. We

then set our work apart from prior surveys on sensor

networks. In the section 3, explores some prominent

routing protocols of sensor network. In the Section

4, comparison of the routing protocols with a

comparative summary of the surveyed approaches

and points out open research problems. Finally

section 5, concludes this paper.

2 SENSOR NETWORK

ARCHITECTURE AND

REQUIREMENTS

Before we discuss the routing protocols, we present

sensor network architecture (10) and requirements.

A sensor network can, in practice, be composed of

tens to thousands of sensor nodes, which are

distributed in a wide area. These nodes form a

network by communicating with each other either

directly or through other nodes. One or more nodes

among them will serve as sink(s) that are capable of

communicating with the user either directly or

through the existing wired networks.

Figure 1: Sensor nodes scanered in a sensor field.

The sensor nodes are usually scattered in a sensor

field as shown in above figure 1. Each of these

sensor nodes has the capabilities to collect the data

and route data back to the sink. Data are routed back

to the sink by a multihop infrastructureless

architecture through the sink as shown in above

figure. The sink may communicate with the task

manger node via Internet or satellite.

Figure 2: Components of Sensor Node.

Figure. 2 shows components of sensor node in which

each node typically consist of the five components:

sensor unit, analog unit, analog digital convector

(ADC), central processing unit (CPU), power unit

and communication unit. They are assigned with

different tasks. The sensor unit is responsible for

collecting information as the ADC requests, and

returning the analog data it sensed. ADC is a

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translator that tells the CPU what the sensor unit has

sensed, and also informs the sensor unit what to do.

Communication unit is tasked to receive command

or query from, and transmit the data from CPU to

the outside world. CPU is the most complex unit. IT

interprets the command or query to ADC, monitors

and controls power if necessary, processes received

data, computes the next hop to the sink, etc. Many

other units may be added for special usage, but the

above five are the most important ones and are

included in every sensor node.

Following are some of the features and requirements

of a sensor network -

• Varying network size – The size of a sensor

network can vary from one to thousands of

nodes.

• Long lifetime network – An important

characteristic of a sensor network is to design

and implement efficient protocols so that the

network can last as long as possible.

• Self – Organization – Sensor nodes should be

able to form a network automatically without

any external configuration.

• Query and re – tasking – The user should be

able to query for special events in a specific

area, or remove obsolete tasks from specific

sensors and assign them with new tasks. This

saves a lot of energy when the tasks change

frequently.

• Cooperation/ Data aggregation – Sensor nodes

should be able to work together and aggregate

their data in a meaningful way. This could

improve the network efficiency.

• Node Capabilities – In a sensor network,

different functionalities can be associated with

the sensor nodes. These special sensors either

deployed independently or the functionality

can be included on the normal sensors to be

used on demand. Reading generated from

these sensors can be at different rates, subject

to diverse quality of service constraints and

following multiple data delivery models, as

explained earlier. Therefore, such a

heterogeneous environment makes data

routing more challenging.

3 ROUTING PROTOCOLS

In this section, we classify the routing protocols (9)

for sensor networks first and then analyze the

existing routing protocols.

A. Classification of the Routing Protocols

Depending on how the sender of a message gains a

route to the receiver, routing protocol can be

classified into three categories, namely, proactive,

reactive and hybrid protocols. In proactive protocols,

all routes are computed before they are really

needed, while in reactive protocols routes are

computed on demand. Hybrid protocols use a

combination of these two ideas. Since sensor nodes

are resource poor, and number of nodes in the

network could be very large, sensor nodes cannot

afford the storage space for “huge” routing tables.

Therefore reactive and hybrid routing protocols are

attractive in sensor networks. According to the

participating style routing protocols can be classified

into three categories, namely, direct communication,

flat and clustering protocols.

B. Existing Routing Protocols

I. Low Energy Adaptive Clustering Hierarchy

(LEACH)

LEACH is a clustering – based protocol that utilizes

randomized rotation of the cluster – heads to evenly

distribute the energy load among the sensor nodes in

the sensor network. It assumes that the base station

is fixed and located far from the sensors and all

nodes in the network are homogenous and energy –

constrained. The main energy saving of LEACH

protocols comes from the combination of data

compression and routing. It (i) employs localized

coordination to improve the scalability and

robustness, (ii) uses data fusion to reduce the amount

of information transmitted between the sensor nodes

and a given sink, and (iii) uses dynamic cluster –

heads mechanism to avoid the energy depletion of

selected cluster – heads.

LEACH provides sensor networks with many good

features, such as clustering architecture, localized

coordination and randomized rotation of cluster –

heads; however, it suffers from the following

problems:

• The nodes on the route from a hot spot to the

base station might drain their battery soon,

which is known as “hot spot” problem.

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217

• It cannot be deployed in time critical

applications.

• The assumption about the sink may

not be practical.

II. Power – Efficient Gathering in Sensor

Information Systems (PEGASIS)

PEGASIS (4) is a chain – based power efficient

protocol based on LEACH. It is near optimal

protocol under the following assumption about the

network.

 All nodes have location information about

all other nodes and each of them has the

capability of transmitting data to the base

station directly.

 Sensor nodes are immobile.

Figure 3: Token passing approach in PEGASIS.

Because each node has global knowledge of the

network, the chain can be constructed easily by

using a greedy algorithm. To balance the overhead

involved in communication between the leaders (s3

is the leader in above figure) and sink, each node in

the chain takes turn to be the leader. Nodes fuse the

received data with their own data when data are

transmitted in the chain.

PEGASIS outperforms LEACH by eliminating the

overhead of dynamic cluster formation, minimizing

the sum of distances that non – leader nodes must

transmit and limiting the number of transmissions.

However, PEGASIS has the same problem as

LEACH does, (about the sink) because of their

common assumptions. Furthermore, it requires

global information of the network known by each

sensor node. This does not scale well and is not

suitable for sensor networks where such global

knowledge is nit easy to obtain.

III. Threshold Sensitive Energy Efficient Sensor

Network Protocol (TEEN)

TEEN (8) is a cluster- based routing protocol based

on LEACH. Before we go into details of TEEN, let’s

have a look at the definition and assumptions used in

TEEN –

Definition

 Hard Threshold (HT) – The absolute value

of the attribute beyond which, the node

sensing this value must switch on its

transmitter and report it.

 Soft Threshold (ST) – A change in the

value of the sensed attribute, which

triggers the node, to switch on its

transmitter and report sensed data.

Assumptions

 The network is composed of a base station

and sensor nodes with the same initial

energy.

 The base station has a constant power

supply and can transmit with high power

to all the nodes directly.

TEEN employs LEACH’s strategy to form clusters,

so all the issues that are un – addressed by LEACH

are left un – addressed by TEEN as well. In addition

to LEACH’s drawbacks TEEN suffers from the

following disadvantages:

 Cluster heads have to leave their

transmitter on all the time and wait for data

sent from other nodes.

 A node’s time slot is wasted if it does not

have data to send, while other nodes have

to wait for their time slots.

 There is no mechanism to distinguish a

node, which does not sense a “big” change

from a dead or failed node.

IV. Sensor Protocols for Information Via

Negotiation (SPIN)

SPIN (6) is family of protocols that efficiently

disseminate information among sensor nodes in

energy – constrained sensor network, assuming all

of them are potential sink. Every node uses meta –

data, i. e. high – level data descriptors, to name their

data and uses negotiations to eliminate the redundant

data transmissions throughout the network.

Conventional data dissemination approaches, e. g.

classic flooding and gossiping, have three problems,

namely, implosion, overlap, and resource blindness.

SPIN solves these problems by using data

negotiation and resource – adaptive algorithms.

Before any data is really transmitted, a node

performs meta – data negotiations. Exchanging

ADV and REQ messages between the sender and

receiver does the negotiations. In addition, SPIN

checks the current energy level of nodes and adapts

the protocol it is running based on how much energy

remains. Simulation results show that SPIN is more

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energy – efficient than flooding or gossiping while

distributing data.

SPIN disadvantages are clear. First of all, it is not

scalable. Secondly, the nodes around a sink could

deplete their battery quickly if the sink is interested

in too many events. Finally, events are always sent

throughout the network.

V. Directed Diffusion

Directed Diffusion (5) is a data – centric routing

algorithm in which all communication is for named

data.. It consists of four elements – interests, data

messages, gradients and reinforcements. An interest

is a task description, which is named by, for instance

a list of attribute – value pairs that describe a task. A

gradient specifies both data rate and the direction

along which events should be sent. Reinforcement is

used to select a single path from multiple paths.

Although this protocol achieves some energy saving,

it also has problems. For instance, to implement data

aggregation, it employs time synchronization

technique, which is not easy to realize in a sensor

network. One other problem in data aggregation is

the overhead involved in recording information.

VI. Flooding and Gossiping

Flooding and gossiping (15) are two classical

mechanisms to relay data in sensor networks without

the need for any routing algorithms and topology

maintenance. In flooding, each sensor receiving a

data packet broadcasts it to all of its neighbors and

this process continues until the packet arrives at the

destination or the maximum number of hops for the

packet is reached. On the other hand, gossiping is a

slightly enhanced version of flooding where the

receiving node sends the packet to a randomly

selected neighbor, which picks another random

neighbor to forward the packet to and so on.

Flooding has several drawbacks. Such drawbacks

include implosion caused by duplicated messages

sent to same node, overlap when two nodes sensing

the same region send similar packets to the same

neighbor and resource blindness by consuming large

amount of energy without consideration for the

energy constraints. Gossiping avoids the problem of

implosion by just selecting a random node to send

the packet rather than broadcasting. However, this

cause delays in propagation of data through the

nodes.

VII. Maximum Lifetime Energy Routing

Chang et al. (16) presents an interesting solution to

the problem of routing in sensor networks based on a

network flow approach. The main objective of the

approach is to maximize the network lifetime by

carefully defining link cost as a function of node

remaining energy and the required transmission

energy using that link. Finding traffic distribution is

a possible solution to the routing problem in sensor

networks and based on that, comes the name

“maximum lifetime energy routing”. The solution to

this problem maximizes the feasible time the

network lasts. In order to find out the best link

metric for the stated maximization problem, two

maximum residual energy path algorithms are

presented and simulated. The two algorithms differ

in their definition of link costs and the incorporation

of nodes’ residual energy.

By using Bellman-Ford shortest path algorithm for

the above link costs, the least cost paths to the

destination (gateway) are found. The least cost path

obtained is the path whose residual energy is largest

among all the paths. The algorithms utilizing these

link costs are compared to Minimum transmitted

energy (MTE) algorithm, which considers ij e as the

link cost. Simulation results show that the proposed

maximum residual energy path approach has better

average lifetime than MTE for both link cost

models. This is due to the absolute residual energy

metric that MTE uses. The newly proposed metrics

are concerned with relative residual energy that

reflects the forecasted energy consumption rate.

4 COMPARISON

In this section, we compare and contrast the routing

protocols for sensor networks, discussed above, with

respect to a few metrics we identified.

As we observe LEACH, PEGASIS and TEEN are

protocols with similar features designed with the

similar idea. It is hard to say one protocol is better

than another one because sensor network are

application specific. For example, SPIN should be

one of the best protocols for application deployed in

a small

sensor network in which no mobility is

required and each node can serve as a sink.

Based on the analysis of the above protocols, we

believe that some of the desirable features of a good

routing algorithm for sensor networks are:

 Dynamic clustering architecture – It

prevents cluster heads from depleting their

power soon and hence extends the

network’s lifetime.

 Data fusion – If nodes could classify and

aggregate data, it helps in efficient query

A SURVEY OF SENSOR NETWORK AND RELATED ROUTING PROTOCOLS

219

processing and decreases network overhead

dramatically. This saves energy.

 Randomizing path choice – If a routing

algorithm can support multiple paths to a

destination with low overhead, it could

help balancing the network load and

tolerating the failure of nodes.

 Thresholds for sensor nodes to transfer

sensed data – Given good threshold it may

solve “hot spot” problem.

 Thresholds for sensor nodes to relay data –

Determining appropriate thresholds of

energy and time delay to relay data would

help in elongating nodes’ lifetime.

5 CONCLUSIONS

Sensor networks that are capable of sensing various

physical phenomena will become ubiquitous in the

near future. Hence, designing efficient routing

protocols for sensor networks that suits sensor

networks serving various applications is important.

In this paper, we identified some of the important

desired features of routing protocols for sensor

networks and also compared and contrast the

existing routing protocols.

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