chapter 2 terrain investigations of routing protocols in wireless...
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CHAPTER 2
TERRAIN INVESTIGATIONS OF ROUTING PROTOCOLS
IN WIRELESS SENSOR NETWORKS
2.1 Introduction
In this Chapter, we focused on static, distance vector and on demand based routing
protocols of wireless sensor networks over linear and service life estimator battery models.
The impact of different wireless sensor networks routing protocols has been judged for
average jitter, first and last packet received, total bytes received, average end to end delay,
throughput and energy consumption. For optimal performance of wireless sensor networks,
challenging issues like energy consumption, network routing, localization, coverage and
physical environment are need to be addressed. Low power and inexpensive nodes are
required to meet the performance goal of the wireless sensor network system. Analytical
modeling and real performance prediction of WSN is extremely critical to measure. This
Chapter emphasized towards the network routing protocol estimations with two battery
models in order to achieve the optimal results for the proposed scenario.
2.2 Bellman-Ford Routing Protocol - Static Protocol
This protocol is based on the Bellman-Ford algorithm also called Bellman-Ford Moore
algorithm. It computes a shortest path tree (SPT) and calculates the minimum path for all
vertices in a weighted digraph [Richard, 1958] through a single source vertex from each
router to other routers in a routing area. In contrast to Dijkstra algorithm, it was slower but
more versatile, as it handles the negative edge weights. For many applications, we need
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negative cycled graphs; hence it becomes useful [Ford et al., 1962]. In case of negative
cycled graphs, early detection is possible through Bellman-Ford algorithm but correction is
not possible for the same [Moore et al., 1959]. For the implementation of the list where the
nodes based on first come first serve principle, Bellman-Ford is surely beneficial. Cheng et
al. [1989] analyzed the Bellman-Ford algorithm for its extended version without bouncing
effect. A wireless sensor network evaluation with loop free Bellman-Ford protocol was
reported by Baharloo et al. [2009].
2.3 Routing Information Protocol - Distance Vector Based
A widely used protocol for wireless networks is routing information protocol (RIP) which
is suitable for both local and wide area. It is similar to open shortest path first (OSPF)
protocol. It can be categorized as an interior gateway protocol (IGP) using a distance vector
routing algorithm. Hedrick [1988] proposed the initial stage of this protocol in 1988 which
was further refined by Malkin [1997]. Advanced techniques such as OSPF and OSI
protocol IS-IS have been supported by routing information protocol as reported in reference
[Malkin, 1998]. As far as the merits of RIP are concerned, it is easily configurable, support
load balancing and loop free. On the contrary, RIP can measure maximum fifteen hops and
shows slower performance when used for a very large scale networks [Ghaleb et al., 2011].
2.4 Dynamic Source Routing Protocol - On Demand Based
Dynamic source routing (DSR) protocol is an on demand routing protocol and specifically
designed for the multi-hop wireless networks [David, 1994]. The major difference between
this protocol and other on demand routing protocols is that it is beaconless and does not
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require periodic beacons. The DSR protocol provides a lot of characteristics like self-
configurability, self-adaptability that makes it network efficient [Johnson et al., 1996]. The
DSR protocol allows the dynamic discovery of the source node and destination node in the
network. It constitutes order lists of nodes that contain all the information about the data
packet life cycle like beginning stage, intermediate stage and final stage. From the
functionality point of view, the DSR contains two mechanisms namely - route discovery
and route maintenance. In the first mechanism, a node wishing to send a packet to a
particular node obtains a source route from that node and route will be discovered only if it
does not already exist. In the second mechanism, a node detects the route with earlier
discovered route which is subjected to the inclusion of network topology change. Route
maintenance mechanism is required only in case of packet transmission breakage between
nodes. Route maintenance mechanism and route discovery mechanism are demand specific
in their nature i.e. on demand type. As compared with other protocols, the DSR requires no
periodic packets and no periodic routing advertisement like link status or neighboring
packet detection [Broch et al., 1999]. These properties take packet overhead to a minimum
value correspond to the stationary nodes. When the nodes are mobile, the routing packet
overhead scales automatically to the required number of tracks as needed. This allows the
routing protocol to behave appropriately in both the conditions either nodes are static or
dynamic.
2.5 Ad Hoc on Demand Distance Vector Routing Protocol
The AODV routing protocol basically extends Bellman-Ford distance vector algorithm
concept in a relative manner. The AODV routing protocol was specifically designed for the
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highly dynamic wireless networks [Perkins et al., 1994; Perkins et al., 2003; Sklyarenko,
2006]. But the un-predictable topology change in wireless sensor network by node failure
makes them virtual dynamic networks. Hence, reactive routing protocols represent an
adequate choice for event driven or periodic data driven WSN applications especially.
Being a reactive type of protocol, routes are created only when required. The AODV
routing protocol stores one entry per table and a sequence number as similar to the
traditional approach of routing to maintain up to date routing information. The AODV
ensures loop free routing in the different situations. This protocol stick towards the time
based state information with each node so that any node that is not recently used should be
treated as dead node. The AODV routing protocol constitutes the traditional concept of
routing table. It stores parameters such as routing information, next hop address, a sequence
number and node usages. It is because of the fact that the node maintains a specific time
span thereafter its entry should be discarded [Chearon et al., 2010]. In case of any link
failure, the neighboring node should be notified about it. In AODV, routing can be
determined by two cycles: query and reply. This protocol uses four control messages
namely: Routing request message (RREQ), Routing reply message (RREP), Routing error
message (RERR) and HELLO message. During the execution, first a node broadcasts a
RREQ message to another node, after that the RREP message is received in the unicast
manner. Further in case of link failure, an error message RERR conveyed to the
neighboring nodes [Sundararajan et al., 2010]. The HELLO message is used for evaluation
and detection of the links between the various nodes.
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2.6 Dynamic on Demand Based Protocol
One of the simple and fast routing protocols for multi-hop networks is a dynamic mobile
ad-hoc network on demand routing protocol (DYMO) [Chakeres et al., 2007; Chakeres et
al., 2010]. It discovers the routes in an on demand fashion and offering enhanced coverage
for dynamic topologies in the wireless networks. Similar to AODV, the source sends a data
packet with a RREQ message to discover the route. The DYMO router waits for a route
after issuance of the RREQ message. If during the waiting period route is not obtained, it
may issue another RREQ. It uses an exponential back off mechanism to reduce the
congestion in the network. Data packets are buffered which are still to be routed as per the
predefined size whereas older packet being discarded accordingly. A RERR message is
issued if a data packet cannot be delivered to the destination due to missing route. In each
DYMO router, little state information like the active source and destination is maintained
because the applicable devices such as WSN have memory constraints. Next sections
summarized the brief description of battery models used for our proposed WSN evaluation.
2.7 Linear Battery Model
This model uses coulomb counting technique as its basis for operation. The coulomb
counting technique accumulates [Rakhmatov et al., 2003; Pedram et al., 2002; QualNet
4.5.1 Wireless Model Library, 2008] the dissipated coulombs from the beginning of the
discharge cycle. It estimates the remaining capacity by measuring the difference between
the accumulated value and a prerecorded full-charge capacity. In the variable load
condition, this method might lose accuracy as it ignores non linear discharge effect. The
battery is discharged in a linear fashion as a function of discharge current load.
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2.8 Service Life Estimator Battery Model
This battery model uses modular approach [Rakhmatov et al., 2003; Pedram et al., 2002;
QualNet 4.5.1 Wireless Model Library, 2008] and can estimate the service life of a battery
operated node with time varying load for an event driven scenario. On the underlying side,
this battery model deploys the tightly coupled component methodology as suggested by
Sarma and Rakhmotav [2003]. For the evaluation, Rakhmotav model remains the most
accurate model than other models by using partial differential equation. For estimation
purposes, one can utilize the following equations (2.1) to (2.3) under constant load [Pedram
et al., 2002]:
(2.1)
Where L denotes life time, m reflects observed lifetimes, and represents objective
specific parameters. The battery voltage changes with time from open-circuit value (Vopen)
to some cutoff value (Vcutoff) for a mentioned load. The observed lifetime denotes that
battery voltages reaches Vcutoff and predicted time denotes the time for which equation (2.1)
holds for a given set of constant loads correspond to the observed lifetimes. To achieve the
objective to match predicted lifetime closely to observed lifetimes this is hard as for
equation (2.1). Another way is to fit the load value for a given set of observed lifetimes.
Assume be the fitted value for I (k) and according to reference [Rakhmatov et al.,
2003]
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(2.2)
One can employ a standard least-squares estimator method to find matches I(k) as closely
as possible for all. The selection should be as per the following model parameters.
(2.3)
is minimized.
2.9 Performance Evaluations of Routing Protocols
We conducted extensive simulations to evaluate the performance of two battery models
with Bellman-Ford, RIP, DSR, AODV and DYMO routing protocols. Simulations are
implemented on QualNet 5.0.2 software package [Scalable Network Technologies, 2003], a
discrete event simulator and capable of simulating both the wired or wireless scenarios
from simple to the complex situations. A block diagram representing the entire simulation
process is shown in figure 2.1.
Figure 2.1: Simulation block diagram for battery models with routing protocols
In the simulation model, there are 100 nodes connected to one wireless station with terrain
dimensions 1500 m × 1500 m as flat area and attitude range above and below sea level is
General
Parameters
Configuration
Set
Nodes
Topology
Place Node
and Set
Mobility
Configure
Wireless
Environment
Set
Routing
Protocol
Statistics and Packet
Tracer Configuration
Parallel
Simulation
Run Time
Optimization Output
Input
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1500 m. The entire area is further divided into 225 square shaped cells. Both the static or
dynamic nodes are considered. This simulation is used with the IEEE standard 802.11 as
distributed coordination function (DCF) for MAC layer protocol. The propagation model
used is two-ray with 2 Mbps radio bandwidth and one communication channel with 2.4
GHz frequency. The traffic type is constant bit rate (CBR). The selection of source and
destinations for each CBR is done in a random manner. The flow of data for each source
and destination node remains constant during the lifetime of a simulated execution which
lasted for 1200 seconds. The mobility model is a random way point with the speed ranging
from 0 m/s to 20 m/s and a pause time of 30 seconds. Numbers of CBR flows are ten with
mobility interval 100 msec in all simulation sets. Table 2.1 shows the summarization of
parameters used in simulation setup.
Table 2.1: Simulation Parameters
Parameters Value
Terrain Dimensions 1500 m × 1500 m
Altitude Above Sea Level 1500 m
Simulation Time 1200 s
No. of Nodes 100
Mobility Interval 100 msec
No. of Channel 1
Channel Frequency 2.4 GHz
No. of CBRs 10
MAC Protocol 802.11
Node Placement Random
Traffic Type CBR
Data Rate 2 Mbps
Mobility Model Random Waypoint
Network protocol IPv4
Routing protocol Bellman-Ford, AODV, DSR, DYMO, RIP
Battery Models
Battery Type
Linear, Service Life Estimator
DURACELL (AA)
Battery Charge Monitoring Interval 60 s
Temperature 290 K
Antenna Model Omni directional
Path-loss Model Two-Ray
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We have collected data for seven performance metrics namely: average jitter, first packet
received, last packet received, total bytes received, average end to end delay, total byte
received, throughput and energy consumption. The first six metrics are evaluated in all
simulation set. The energy consumption is also evaluated separately for linear (LN) and
service life estimator (SLE) battery models within the deployed scenario.
2.9.1 Average Jitter Analysis
In our evaluation, we observed average jitter accuracy of five protocols with the service life
estimator model as shown in figure 2.2. Average jitter denotes the time variable measured
between the arrival of the packets (due to the congestion of the network), timing drift or
route change.
Figure 2.2: Graph of average jitter versus routing protocols over SLE model
40 50 60 70 80 90 1000
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
Nodes
Avera
ge J
itte
r (s
)
Bellman Ford
RIP
DSR
AODV
DYMO
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We calculated value of average jitter at ten randomly selected nodes - 47, 50, 54, 63, 74,
84, 87, 95, 98, 99 with a contestant bit rate. We assumed that at least ten percent nodes are
participating in communication and rests of the nodes are connected to a central base
station all the times. These nodes act as a representative for the entire network. We
observed that if we change the node value then we get the same resultants in all the cases.
The average jitter values of the Bellman-Ford protocol outperform other protocols for
nodes 47, 50, 54,74,82,98. In the context of the Bellman-Ford protocol, a novel analytical
model for wireless sensor network based on the Markovian general distribution with one to
k serves (M/G/1/k) queuing system and Bellman-Ford routing strategies to predict average
message latency was reported by Baharloo et al. [2009]. We extended similar work towards
the estimation of Bellman-Ford protocol performance based on average jitter, packet
delivery, throughput and end to end delay for the same in contrast with other protocols. RIP
protocol overweigh other protocols for the node value 63, 87 and 95. DYMO performs
better in case of node ninety-nine as compared to rest of the protocols. Ghaleb et al. 2011
made a comparative analysis among DSR and RIP protocol and investigated that RIP
outperforms DSR in case of average jitter. Our results show good agreement with the
results reported in reference [Ghaleb et al., 2011]. It is further observed that initially
Bellamn-Ford protocol reflect minimum average jitter than other protocols and at last
DYMO protocol overweigh other protocols.
2.9.2 Packets and Total Bytes Reception Analysis
Secondly, we estimated the time span for the first packet and last reception in all the
protocols as shown in figure 2.3 and figure 2.4.
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Figure 2.3: Graph of first packet reception versus routing protocols for SLE model
We can rank them in order of first packet reception as (i) Bellman-Ford (ii) AODV (iii)
DSR (iv) DYMO (v) RIP. As far as last packet reception is concerned, the small change in
sequence observed as (i) Bellman-Ford (ii) DSR (iii) RIP (iv) DYMO (v) AODV. Again,
Bellman-Ford outperforms rest of the protocols at the first node i.e. node 47, as depicted in
figure 2.4 whereas DYMO outperform other protocols for last node i.e. node 99 in case of
first packet reception analysis. The AODV protocol analysis also shows good performance
in the above said evaluation. We analyzed that functionality involved in a specific protocol
for its operation is responsible for the same. We observed that for a small network,
Bellman-Ford outperforms other and in the case of large networks, DYMO shows better
behavior.
40 50 60 70 80 90 1001
1.5
2
2.5
3
3.5
4
4.5
Nodes
First
packet
Receiv
ed (
s)
Bellman Ford
RIP
DSR
AODV
DYMO
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Figure 2.4: Graph of last packet reception versus routing protocols for SLE model
We estimated the total bytes received at the nodes after the deployment of these five
protocols as shown in figure 2.5. It has been observed that DSR outperforms rest of the
protocols because of the byte destruction rate remains quite less than other protocols. As
shown in figure 2.5, the order of error proneness during bytes transmission for these
protocols can be mentioned as (i) DSR (ii) DYMO (iii) AODV (iv) RIP (v) Bellman-Ford.
This is due to the fact that lesser the bytes received during the transmission depict lesser
error proneness of these protocols. In the context of AODV protocol, three optimizations
were studied by Lee et al. [2003]. Theses optimization includes ring search, a query
localization protocol and local repair mechanism. Bai et al. [2006] made a comparison
40 50 60 70 80 90 10024
24.01
24.02
24.03
24.04
24.05
24.06
24.07
24.08
Nodes
Last
Packet
Receciv
ed (
s)
Bellman Ford
RIP
DSR
AODV
DYMO
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among AODV and DSR protocols and proposed a new protocol incorporating features of
these two protocols.
Figure 2.5: Graph of total byte reception versus routing protocols over SLE model
We have already evaluated the performance of the dynamic source protocol in wireless
sensor network in the reference [Verma et al., 2011]. Also, we have presented the
behavioral assessment of the AODV protocol over temporal constraints in wireless sensor
network in the reference [Verma et al., 2012]. A cross layer design pattern of AODV for
multi-hop flow of the wireless network was suggested by Chou et al. [2013]. We extended
this work towards the evaluation two battery models with five different routing protocol
including AODV and DSR.
40 50 60 70 80 90 1001.06
1.08
1.1
1.12
1.14
1.16
1.18
1.2
1.22
1.24x 10
4
Nodes
Tota
l B
yte
s R
eceiv
ed
Bellman Ford
RIP
DSR
AODV
DYMO
36
2.9.3 Throughput Analysis
This refers to the number of delivered packets per unit of time within the network. Figure
2.6 shows the throughput of Bellman-Ford, RIP, DSR, AODV and DYMO for our
proposed simulation model. Raghuvanshi et al. [2010] reported that an average throughput
remains highest for DYMO protocol and energy consumption remains least when used with
25% duty cycle at MAC in contrast with AODV routing protocol.
Figure 2.6: Graph of throughput versus routing protocols over SLE model
In our evaluation, we extended this work a bit intricate level and evaluated the DYMO and
DSR protocol in contrast with AODV, RIP and Bellman-Ford. We observed that DYMO
and DSR exhibits the maximum throughput than other protocols for most of the nodes,
whereas Bellman-Ford shows the decrement in behavior for throughput in approximately
40 50 60 70 80 90 1003900
4000
4100
4200
4300
4400
4500
Nodes
Thro
ughput
(bits/s
)
Bellman Ford
RIP
DSR
AODV
DYMO
37
all the cases. We analyzed that the enhanced mechanism involved in DYMO and DSR
protocols with lowers the route failure rate, resulting in better throughput all the time as
compared to other protocols in the scenario.
2.9.4 Average End to End Delay Performance
This refers to the time from source to destination node taken by a packet across the
network. This includes transmission resultant, propagation and processing delay or all
possible delays which can occur during packet transmission. Figure 2.7 shows the average
Figure 2.7: Graph of average ETE delay versus routing protocols SLE model
end to end delay of Bellman-Ford, RIP, DSR, AODV and DYMO. We observed that
Bellman-Ford exhibits the lowest end to end delay for most of the times. At the end point,
40 50 60 70 80 90 1000
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Nodes
Avera
ge E
nd-t
o-E
nd D
ela
y (
s)
Bellman Ford
RIP
DSR
AODV
DYMO
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i.e. node 99, the AODV protocol shows better behavior than other protocols and exhibits
lower end to end delay. It has been observed that end to end (ETE) delay in the figure 2.7
does not show an increasing trend. This is due to a constant number of CBR sources used in
our model. We again observed that for smaller networks, Bellman-Ford outperforms than
the other protocols because of the shorter average route even if used in congested networks.
On the other hand, the AODV performs better due to less congestion in the larger networks
even if consumes more average route length. Manju et al. [2013] reported that there exists a
trade-off between delay and throughput in case of the DYMO protocol which remains true
in our case as the delay remains most of the times. Raghuvanshi et al. [2010] shows that
DYMO exhibits better performance when using linear battery model than AODV, DSR and
Bellman-Ford in terms of throughput on the scarification of end to end delay aspect. We
extended this work by incorporating RIP protocols and using service life estimator battery
model in the comparative evolution of these protocols.
2.9.5 Energy Consumption
The energy consumption issue always remains as a major concern in wireless sensor
networks. We calculated the average energy consumption by Bellman-Ford, RIP, DSR,
AODV and DYMO protocols over linear and service life estimator models in wireless
sensor network. Figure 2.8 compares five routing protocols from energy consumption
aspect. A comparative analysis about the energy consumption with respect to sensors value
increment was reported by Chearon et al. [2010]. In our proposal, we extended this concept
towards the different WSN routing protocols by correlating these protocols with different
battery models. We observed that DYMO protocol consume minimum power in both the
cases of linear and service life estimator battery model than other protocols as shown in
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figure 2.8. This is because of the fact that the residual battery capacity remains a maximum
in the same case. Li. et al. [2007] reported that under the linear model, DYMO protocol
represents best behavior when compared with Bellman-Ford, DSR and AODV. We further
extended this comparative analysis to RIP protocol with a service life estimator model in
our consideration. We analyzed that as far as the maximum power consumption concerned,
Bellman-Ford consumes maximum power in both the cases as it shows less residual battery
capacity. According to figure 2.8, x-axis denotes routing protocols: 1 Bellman-Ford, 2 RIP,
3 DSR, 4 AODV and 5 DYMO and y-axis represent battery capacity in mAh.
Figure 2.8: Energy consumption of LN and SLE battery models versus routing protocols
1 1.5 2 2.5 3 3.5 4 4.5 51000
1200
1400
1600
1800
2000
2200
2400
2600
2800
Rouing Protocols
Resid
ual B
att
ery
Capacity (
mA
.h)
Linear Model
Service Life Estimator Model
40
2.10 Summary
In this Chapter, we analyzed the five WSN routing protocols and two battery models in the
literature and explored them in details. We summarized that the current state of art in these
models. Moreover, the quality of services specific aspects like average jitter, packets
delivery, throughput, end to end delay and energy consumption have been analyzed for the
different routing protocols. Finally, we investigated towards the implementation and
assessment of routing protocols in our simulation model. It has been concluded that DYMO
routing protocol performance overweighs the rest of the protocols in our proposal and
service life estimator model outperforms linear model in all the cases of energy
consumption.
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