Iaetsd a survey on geographic routing relay selection in

A Survey on Geographic Routing Relay Selection in
Wireless Sensor Network
Santosh Kumar K1
, Dr.N.Duraipandian2
1. PG Scholar, Department of Computer Science and Engineering, Velammal Engineering College, Chennai, Tamilnadu, India.
2. Professor, Department of Computer Science and Engineering, Velammal Engineering College, Chennai, Tamilnadu, India.
Email: santhosh2567@gmail.com1
, emailpandiandurai@gmail.com2
ABSTRACT: Geographic routing (or
position-based routing) is the technique
which employs position information of nodes
while routing from the source to the sink.
Geographic routing has been considered as a
simple, effective and scalable routing
protocol for designing a variety of
applications ranging from mobility
prediction, management of nodes and
metrics such as hop count, power, relay
selection, energy consuming, delay, etc. Most
geographic routing algorithms use a greedy
strategy for selecting the neighbor closest to
the sink as a next hop. However, greedy
forwarding fails in reaching a node that is
closer to the sink than all its neighbors and
so planar graph routing is adopted which
guides the packet with guarantees delivery.
Geographic routing algorithms exploit
location information but the problem exist is
convergecasting around connectivity holes
and relay selection of each node. For
resolving these issues, an alternative method
termed ALBA_R was proposed along with
enhanced relay selection mechanism in order
to maximum the lifetime of a node.
I. INTRODUCTION
A Wireless Sensor Networks (WSN) has been
emerged as a promising area for research and
scientific advancement. WSNs are greatly
applied in many application domains such as
surveillance, environmental monitoring,
vehicular applications, defense applications,
traffic systems, medical monitoring, precision
agriculture, etc. WSN consist of nodes with
sensing (measuring), computing, and wireless
communications capabilities. Technology
upgradation in WSN is the result of integration
of sensor, transceiver, memory and
microcontroller technologies on single unit
called sensor node. The Sensor Node, a basic
element of WSN capable of observing(sensing)
physical capabilities, process the monitored and
received information and communicate the
observed or processed information to the nearby
sensor nodes to form a network of sensor nodes.
Fig.1 components of a sensor node
Many sensor nodes are randomly distributed
over larger distance and each sensor node
having data content were gathered in the sink.
Through internet everyone can view the
collected data in the network. As shown in
Fig.1, it consists of three major components
namely Sensing unit, processing unit, and
Transmission unit. They include some
additional components like position finding
system, power supply and a mobilizer. Sensing
unit composed of two subunits such as Sensors
and Analog-to-Digital Converters (ADCs).The
analog signals are measured by the sensors are
digitized through an ADC and in turn fed into
the processing unit (storage unit and a
processor). The processor and its associated
memory commonly RAM is used to manage the
procedures that make the sensor node carry out
its assigned sensing and collaboration tasks. In
Transmission unit, the transceiver connects the
node with the network and serves as the
communication medium of the node. The power
supply/battery is the most important component
of the sensor node because it implicitly
determines the lifetime of the entire network.
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A major evolution in communication
technology has been the introduction of the
Global Positioning System (GPS) which
provides the location information and universal
timing of a node. The recent development of a
wireless sensor network has led to an innovative
use of small sensory nodes which operate with a
very low power in extreme environmental
conditions. A group of small sensory nodes are
randomly deployed in a sensor field. These
nodes have the ability to organize themselves
automatically and to detect the data content
accurately.
II. LITERATURE REVIEW
A New Contention-Based MAC Protocol for
Geographic Forwarding in Ad Hoc and
Sensor Networks [1]:
Michele Zorzi referred that Geographic
Random Forwarding (GeRaF) is based on the
assumption that sensor nodes have a means to
determine their location information, and that
the positions of the final destination and of the
transmitting node are explicitly included in each
message. GeRaF is designed to integrate MAC
message exchanges and the designation of the
most convenient relay (from a geographic point
of view). Thanks to awake/sleep cycles and to
this cross–layer design, it is very energy–
efficient. Moreover, it is simple and easy to
implement on real nodes. However, it has some
drawbacks, e.g., it cannot route around
connectivity holes, and thus may not be able to
deliver all messages in sparse networks
(because of the physical absence of nodes).
Also, it is not able to operate in dense traffic
scenario (when congestion builds up).
On the Effect of Localization Errors on
Geographic Face Routing in Sensor
Networks [2]:
The reason for geographic routing protocols
does not need to maintain per destination
information and only neighbor location
information is needed to route packets.
Geographic routing protocols are very attractive
choices for routing in sensor networks. Most
geographic routing protocols use greedy
forwarding for basic operations. Greedy
forwarding is based on next forwarding hop is
chosen to minimize the distance of the
destination. It fails in dead-ends. Most
geographic routing protocols use greedy
forwarding for basic operations. In order to
provide correct routing in the presence of dead
ends, face routing has been introduced. GPSR is
a geographic routing protocol for wireless
networks that combines greedy forwarding and
face routing. GPSR uses geographic hash table
(GHT) system that hashes keys into geographic
location and stores the key-value pair at the
sensor node closest to the hash of its key.GHT
uses mainly for geographic routing to the hash
location. The applications are data centric
storage and distributed indexing.
Locating and Bypassing Holes in Sensor
Networks [3]:
In routing, connectivity holes cause difficulties
in organizing the networks. Holes define the
“hot spots” regions created by traffic congestion
and sensor power shortage. A commonly used
assumption in studying sensor networks is that
sensors are uniformly densely distributed in the
plane. However in system deployment, this
assumption does not hold in general. Even if
sensors nodes are distributed randomly, there
are still regions with sensor density much lower
than others. In practice, sensor networks usually
have holes, i.e. regions without enough working
sensors. An example of a large number of dead
sensor nodes it creates a big hole in the
network. A packet is forwarded to a 1-hop
neighbor who is closer to the destination than
the current sensor node. This process is repeated
until the packet data reaches the destination, or
the packet is stuck at a node when there is no
neighbor to reach the destination. Here, holes
define to be simple regions enclosed by a
polygon cycle which contains all the nodes
where local minima can appear. The
information storage and Memory requirement
are based on boundary node. The applications
are avoiding network hot spots, supporting path
migration. The applications are avoiding
network hot spots, supporting path migration,
information storage mechanisms. It can able to
handle node failures, information storage and
memory requirement. It uses TENT rule and
BOUNDHOLE techniques to identify and build
around holes. TENT rule requires each node to
know its 1-hop neighbors locations. To help
packets get out of stuck nodes, BOUNDHOLE
to find the boundary of the hole.
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Efficient Non-Planar Routing around Dead
Ends in Sparse Topologies Using Random
Forwarding [4]:
Geographic forwarding in wireless sensor
networks (WSN) has long suffered from the
problem of bypassing "dead ends," i.e., those
areas in the network where no node can be
found in the direction of the data collection
point (the sink). However previous scheme
resolve these problems that rely on geometric
techniques leading to the planarization of the
network topology graph. In this paper, a
alternative method to planarization is proposed,
termed ALBA-R, which successfully routes
packets to the sink transparently to dead ends.
ALBA-R combines scheduling of awake or
asleep nodes, channel access and geographic.
By enhancing geographic routing along dead-
ends with a mechanism that is capable of
routing packets around connectivity holes.
Through simulations results, it demonstrates
that ALBA-R can provide insignificant
overhead, and outperforms similar solutions
with respect to all the metrics of interest
investigated, especially specifying the
benchmark for geographic routing protocols..
Localization Error-Resilient Geographic
Routing for Wireless Sensor Networks [5]:
This paper concerns the demonstration of the
resilience to localization errors of ALBA-R, a
protocol for geographic routing in wireless
sensor networks (WSNs). In particular, it shows
that a simple effective nodal coloring
mechanism for handling nodal connectivity
holes, ALBA-R achieves the further desirable
benefit of being totally resilient to localization
errors, which are unavoidable in WSNs.
Through ns2-based simulations it explains that
fundamental network parameters such as
network density, and also independently of
errors in nodal coordinate estimations as high as
the node transmission radius, ALBA-R is
successful in delivering all generated packets
while incurring reasonable degradation for
metrics such as route-length and end-to-end
latency and still remaining and energy efficient
protocol.
ALBA-R: Load-Balancing Geographic
Routing Around Connectivity Holes in
Wireless Sensor Networks [7]:
ALBA-R features the cross-layer integration of
geographic routing with contention-based MAC
for relay selection and load balancing (ALBA),
as well as a mechanism to detect and route
around connectivity holes (Rainbow). ALBA
and Rainbow (ALBA-R) together solve the
problem of routing around a dead end without
overhead-intensive techniques such as graph
planarization and face routing. The Rainbow
mechanism allows ALBA-R to efficiently route
packets out of and around dead ends. Rainbow
is resilient to localization errors and to channel
propagation impairments. It does not need the
network topology to be planar, unlike previous
routing protocols. It is, therefore, more general
than face routing-based solutions and is able to
guarantee packet delivery in realistic
deployments.
III. PRELIMINARY
A. Routing in WSN
Routing is a process of determining a path
between source and destination regarding the
transmission of packet messages. When the
sink is far away from the source or not in the
range of source node, multi-hop technique is
followed. In order to moving a packet of data
from source to destination, intermediate sensor
nodes have to relay their packets efficiently. As
shown in Fig.2 depend on network structure,
routing protocol that perform an end-to-end
message delivery can be classified as flat-based
routing, hierarchical-based routing, and
geographic routing (location-based routing). In
flat-based routing, all the nodes are treated as
equal and ensure same roles or functionality. In
hierarchical-based routing, all the nodes are
assigned with differed roles and provide higher
energy nodes for transmission as well as lower
energy nodes for sensing. In location-based
routing, node’s positions are extracted to route
packet data i.e. Sensor nodes are addressed by
means of their locations and their location were
obtained by distance estimation, neighbor
discovery, GPS etc.
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Fig.2 classification of routing protocols
Based on their protocol operations, routing
protocols are categorized into negotiation-based
routing, QoS-based routing, multipath-based
routing, query-based routing and coherent-
based routing. In negotiation-based routing,
these protocols add top-level data descriptors to
eliminate duplicate data transmissions through
negotiation. In multipath-based routing, it
increases fault tolerant capabilities that routes
the data packet through a path. The path gets
changed whenever a short path is discovered. In
query-based routing, the target nodes transmit a
query of data from a node in the network and a
node with this data that matches the query sends
back to the initial node. In QoS-based routing,
this protocol ensures balance between energy
consumption and data quality in the network.
In coherent-based routing, sensor nodes send
data to an intermediate node where necessary
data can be aggregated and may be subject to
minimum processing. Hence each node can
reduce route cost in terms of energy
consumption. Each of these routing schemes
has the common objective of trying to get better
throughput and to extend the lifetime of the
sensor network.
B. Geographic routing
Geographic routing (also called position-based
routing) is a routing principle that relies on
geographic location information. It is defined
only for wireless networks and based on the
idea that the source sends a message to the
geographic location of the destination instead of
using the network address. In geographical
routing, a sender uses the destination’s
geographic location to deliver a message.
Information of physical location might be
determined by means of a global positioning
technique (GPS) or relative positioning.
Geographical routing assumes that each node
knows its own location and each source is
aware of the location of its destination.
Geographic routing protocols require only local
information and thus are very efficient in
wireless networks.
Geographic Greedy Forwarding:
Fig.3 Greedy Forwarding
An important technique in geographic routing is
greedy forwarding, in which each node should
transfer packet by selecting the neighbor closest
to the destination along the path until the packet
reaches the destination as shown Fig.3. Greedy
forwarding, however, fails in the presence of
connectivity holes or dead-ends when a node
that has no neighbors closer to the destination.
A greedy forwarding can minimize the distance
to reach the destination location but it cannot
assure guarantee in delivery of messages.
Planar Graph Routing:
A widely adopted approach to solve this
guaranteed message delivery is planar graph
routing. Planar graph routing is a key concept
for recovery from a local minimum situation. It
can provide delivery guarantees using face
routing used when greedy forwarding fails. In
order to perform face routing, a planar
connectivity graph for the network needs to be
constructed and so a planarization algorithm is
required to create the planar graph as shown in
Fig.4. Face routing is integrated with greedy
forwarding and is used as a way to overcome
dead-ends when greedy forwarding fails.
Greedy forwarding coupled with face routing is
the common efficient approach of the currently
proposed geographic protocols.
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Fig.4 Planar graph routing
C. Issues in geographic routing
Some of the following issues while designing
geographic routing schemes are
(a)Routing around dead ends,
(b) Flexibility to localization errors,
(c)Relay selection process.
IV. OVERVIEW
D. Relay Selection in Geographic routing
Adaptive load balancing algorithm improves a
convergecasting in WSNs that integrates
awake/asleep schedules, Medium access control
(MAC), load balancing, and back-to-back data
transmissions. While transmission, it is
necessary to ensure that a node which is awake
or asleep or else usage of energy can get
increases. A sensor node can forward packet
from source to destination by sending RTS
(Request-to-send) packet to all the neighbor
nodes in order to ensure their availability of
awake nodes. The sensor nodes which are
available will report with clear-to-send (CTS)
packet carrying information through which the
sender can choose the best relay. A ‘best
throughput’ can get performed through effective
relay selection. Every upcoming relay is
characterized by two parameters: the queue
priority index (QPI), and the geographic priority
index (GPI). The QPI is measured as, the
requested number of packets to be transmitted
in a burst is Nb, and the number of packets in
the queue of an eligible relay is Q. The potential
relay keeps a moving average M of the number
of packets it was able to transmit back-to-back,
without errors, in the last forwarding attempts.
The QPI is then defined as min (Q+Nb)/M,
Nq, where Nq is the maximum allowed QPI.
Rainbow is the mechanism used by ALBA_R to
deal with dead ends. An important feature for
avoiding dead ends is that of allowing the nodes
to forward packets away from the sink when a
relay selection toward the sink cannot be
found. To remember whether to search for
relays in the direction of the sink, each node is
labeled with different colors. Rainbow
mechanism discovers a specific color of each
node so that a possible route to the sink is
determined.
E. Enhanced Relay Selection Scheme
The relay selection scheme of ALBA_R [7] can
fail in two cases:
1. If no node with any QPI is found
2. If the contention among nodes with the
same QPI and GPI is not resolved within
a maximum number of attempts.
Both situations cause the sender to back off. It
will lead to end to end delay also.
To overcome that, we can send the RTS packets
to all the one hop neighbor nodes to collect the
QPI value as the reply instead of sending RTS
with particular QPI value. After receiving QPI
value, we can select the relay node based on the
QPI value in the increasing order. If there is a
tie, we can choose the relay node which is
having lowest GPI value as shown in Fig.5.
Thus the delay will be reduced by transmitting
the RTS packets again and again until find out
the node with particular QPI value.
Fig.5 System architecture
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V. CONCLUSION
WSNs have seen tremendous developments in
design and applications over the recent years.
This speedy progress has resulted in the stress
towards solving the hurdles that this area has to
face. The area of WSN is thriving and every day
new ideas are emerging. The positive benefits
of this are quite obvious; such a technology will
achieve fine granularity tracking of what is
going on at far away and generally in
inaccessible locations. A wireless senor network
is the latest and fastest growing technology and
is expected to revolutionize a wide range of
applications in terms of its quality and
availability in the near future.
REFERENCES
[1]M. Zorzi, “A New Contention-Based MAC
Protocol for Geographic Forwarding in Ad Hoc
and Sensor Networks,” Proc. IEEE Int’l Conf.
Comm. (ICC ’04), vol. 6, pp. 3481-3485, June
2004.
[2]K. Seada, A. Helmy, and R. Govindan, “On
the Effect of Localization Errors on Geographic
Face Routing in Sensor Networks,” Proc.
IEEE/ACM Third Int’l Symp. Information
Processing in Sensor Networks (IPSN ’04), pp.
71-80, Apr. 2004.
[3]Q. Fang, J. Gao, and L.J. Guibas, “Locating
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ACM Mobile Networks and Applications, vol.
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[4]P. Casari, M. Nati, C. Petrioli, and M. Zorzi,
“Efficient Non-Planar Routing around Dead
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Forwarding,” Proc. IEEE Int’l Conf. Comm.
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[5]S. Basagni, M. Nati, and C. Petrioli,
“Localization Error-Resilient Geographic
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IEEE GLOBECOM, pp. 1-6, Nov./Dec. 2008.
[6]A. Camillo, M. Nati, C. Petrioli, M. Rossi,
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[7]C.Petrioli, P.Casari, M.Zorzi, M.Nati, and
S.Basagni, “ALBA-R: Load-Balancing
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[8]S. Ru¨ hrup and I. Stojmenovic, “Optimizing
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[9] H. Frey, S. Ru¨ hrup, and I. Stojmenovic,
“Routing in Wireless Sensor Networks,” Guide
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[10] Fraser Cadger, Member, Kevin Curran,
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[11] Rama Sundari Battula, O. S. Khanna
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Sensor Networks: A Review”, International
Journal of Engineering and Innovative
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2013
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