SlideShare a Scribd company logo
1 of 10
Download to read offline
IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 08, 2014 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 207
An Analytical Model of Latency and Data Aggregation Tradeoff in
Cluster Based Wireless Sensor Networks
Naresh Kumar1
Arvind Sharma2
Gajendra Sajedia3
1
M. Tech Scholar 2,3
Assistance Professor
1,2,3
Department of Electronics and Communication Engineering
1,2,3
Rajasthan Institute of Engineering and Technology
Abstract— Sensor networks are collection of sensor nodes
which co-operatively send sensed data to base station. As
sensor nodes are battery driven, an efficient utilization of
power is essential in order to use networks for long duration.
Therefore it is needed to reduce data traffic inside sensor
networks, thereby reducing the amount of data that is
needed to send to base station. The main goal of data
aggregation algorithms is to gather and aggregate data in an
energy efficient manner so that network lifetime is
enhanced. In wireless sensor network, periodic data
sampling leads to enormous collection of raw facts, the
transmission of which would rapidly deplete the sensor
power. A fundamental challenge in the design of wireless
sensor networks (WSNs) is to maximize their lifetimes. Data
aggregation has emerged as a basic approach in WSNs in
order to reduce the number of transmissions of sensor nodes,
and hence minimizing the overall power consumption in the
network. Data aggregation is affected by several factors,
such as the placement of aggregation points, the aggregation
function, and the density of sensors in the network. In this
paper, an analytical model of wireless sensor network is
developed and performance is analyzed for varying degree
of aggregation and latency parameters. The overall
performance of our proposed methods is evaluated using
MATLAB simulator in terms of aggregation cycles, average
packet drops, transmission cost and network lifetime.
Finally, simulation results establish the validity and
efficiency of the approach.
Key words: Wireless sensor network, Data aggregation,
Latency, Clustering
I. INTRODUCTION
Sensor networks are increasingly deployed for applications
such as wildlife habitat monitoring, forest fire prevention,
and military surveillance [1]. In these applications, the data
collected by sensor nodes from their physical environment
need to be assembled at a host computer or data sink for
further analysis. Typically, an aggregate (or summarized)
value is computed at the data sink by applying the
corresponding aggregate function, e.g., MAX, COUNT,
AVERAGE or MEDIAN to the collected data [2]. In large
sensor networks, computing aggregates in-network, i.e.,
combining partial results at intermediate nodes during
message routing, significantly reduces the amount of
communication and hence the energy consumed. An
approach used by several data acquisition systems for sensor
networks is to construct a spanning tree rooted at the data
sink, and then perform in-network aggregation along the
tree. Partial results propagate level-by-level up the tree [3],
with each node awaiting messages from all its children
before sending a new partial result to its parent. Researchers
have designed several energy-efficient algorithms for
computing aggregates using the tree-based approach. Tree-
based aggregation approaches, however, are not robust to
communication losses which result from node and
transmission failures and are relatively common in sensor
networks. Because each communication failure loses an
entire sub-tree of readings, a large fraction of sensor
readings are potentially unaccounted for at the data sink,
leading to a significant error in the aggregate computed. To
address this problem, researchers have proposed novel
algorithms that work in conjunction with multi-path routing
for computing aggregates in lossy networks.
Nevertheless, data aggregation provides a way to
increase network lifetime of nodes by reducing the number
of transmitted data packets, possibly at the cost of latency.
With the advent of high functionality semiconductor IC's,
any WSN node can be programmed to implement
aggregation function at relatively no additional cost. This
leads to considerable increase in network lifetime.
Moreover, protocols such as LEACH (Low Energy
Adaptive Clustering Hierarchy) ensure evenly dissipation of
energy in cluster nodes thereby ensuring mechanisms to
increase network life at higher layers.
A. Data Aggregation and Data Security
Data acquisition systems for sensor networks can be
classified into two broad categories on the basis of data
collection methodology employed for the application:
 Query-based systems [4]
 Event Based Systems [5]
In query-based systems, the base station (the data
sink) broadcasts a query to the network and the nodes
respond with the relevant information. Messages from
individual nodes are potentially aggregated to reroute to the
base station. Finally, the base station computes one or more
aggregate values based on the messages it has received. In
some applications, queries may be persistent in nature
resulting in a continuous stream of data being relayed to the
data sink from the nodes in the network. For such
applications, the query broadcast by the base station
specifies a period (referred to as an epoch); nodes in the
network send their readings to the base station after each
epoch.
In event-based applications, such as perimeter
surveillance and biological hazard detection, nodes send a
message to the base station only when the target event
occurs in the area of interest. If multiple reports being
relayed correspond to the same event, they can be combined
by an intermediate node on the route to the base station.
Data acquisition systems can also be categorized
based on how sensor data is aggregated. In single-aggregator
approaches, aggregation is performed only at the data sink.
In contrast, hierarchical aggregation approaches make use of
in-network aggregation. Hierarchical aggregation schemes
can be further classified into tree-based schemes and ring-
based schemes on the basis of the topology into which nodes
are organized. Most existing data management and
An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks
(IJSRD/Vol. 2/Issue 08/2014/049)
All rights reserved by www.ijsrd.com 208
acquisition systems for sensor networks are vulnerable to
security attacks launched by malicious parties [6]. Sensor
nodes are often deployed in unattended environments, so
they are vulnerable to physical tampering. Since current
sensor nodes lack hardware support for tamper-resistance, it
is relatively easy for an adversary to compromise a node
without being detected. The adversary can obtain
confidential information (e.g., cryptographic keys) from the
compromised sensor and reprogram it with malicious code.
Moreover, the attacker can replicate the compromised node
and deploy the replicas at various strategic locations in the
network.
A compromised node can be used to launch a
variety of security attacks. These attacks include jamming at
the physical or link layer as well as other resource
consumption attacks at higher layers of the network
software. Compromised nodes can also be used to disrupt
routing protocols and topology maintenance protocols that
are critical to the operation of the network.
By using a few compromised nodes to render
suspect the data collected at the sink, an adversary can
effectively compromise the integrity and trustworthiness of
the entire sensor network. In event-based systems,
compromised nodes can be used to send false event reports
to the base station with goal of raising false alarms and
depleting the energy resources of the nodes in the network.
This attack is referred to as the false data injection attack.
Similarly, in query-based systems, compromised nodes can
be used to inject false data into the network with the goal of
introducing a large error in the aggregate value computed at
the data sink. The aggregate computed by the sink is
erroneous in the sense that it differs from the true value that
would have been computed if there were no false data
values included in the computation. Unlike event-based
systems, however, in aggregation systems the effectiveness
of a false data injection attack depends on both the
aggregate being computed, e.g., MAX or MEDIAN, and
whether sensor data is aggregated to re-route to the data sink
or only at the data sink, and thus the techniques used for
preventing this attack differ from the techniques used in
event-based systems. In an aggregation system, a
compromised node M can corrupt the aggregate value
computed at the sink in four ways. First, M can simply drop
aggregation messages that it is supposed to relay towards the
sink. This has the effect of omitting a large fraction of
sensor readings being aggregated. Second, M can alter a
message that it is relaying to the data sink. Third, M can
falsify its own sensor reading with the goal of influencing
the aggregate value. Fourth, in systems that use in-network
aggregation, M can falsify the sub-aggregate which it is
supposed to compute based on the messages received from
its child nodes. The first attack in which a compromised
node intentionally drops aggregation messages can
substantially deviate the final estimate of the aggregate if
tree-based aggregation algorithms are used. The deviation
will be large if the compromised node is located near the
root of the aggregation hierarchy because a large fraction of
sensor readings will be omitted from being aggregated.
Countermeasures against this attack include the use of
multi-path routing and ring-based topologies as well as the
use of probabilistic techniques in the formation of
aggregation hierarchies. To prevent the second attack in
which a compromised node alters a message being relayed
to the sink, it is necessary for each message to include a
message authentication code (MAC) generated using a key
shared exclusively between the originating node and the
sink. This MAC enables the sink to check the integrity of a
message, and filter out messages that have been altered.
Hence, the effect of altering a message is no different from
dropping it, and countermeasures such as multi-path routing
are needed to mitigate the effect of this attack. The third
attack in which a sensor intentionally falsifies its own
reading is known as the falsified local value attack. This
attack is similar to the behavior of nodes with faulty sensors,
and also to the false data injection attack in event-based
systems. Potential countermeasures to this attack include
approaches used for fault tolerance such as majority voting
and reputation-based frameworks. The three attacks
discussed above apply to both single-aggregator and
hierarchical aggregation systems, whereas the fourth attack
applies only to hierarchical aggregation systems. This attack
in which a node falsifies the aggregate value it is relaying to
its parent(s) in the hierarchy is much more difficult to
address. This attack is referred to as the falsified sub-
aggregate attack.
II. PROBLEM DEFINITION
In wireless sensor network, data fusion is considered as an
essential process for preserving sensor energy. Periodic data
sampling leads to enormous collection of raw facts, the
transmission of which would rapidly deplete the sensor
power. Thus, aggregators are required to aggregate a
collection of samples which can be further transmitted to the
sink. This aggregator can be implemented in an existing
wireless sensor node or can be of a distinct type other than
the nodes. The requirement of aggregator is critical for
increasing the network lifetime as the battery life of
individual nodes can be depleted in sending large amount of
raw data without any aggregation.
However, data aggregation comes with the cost of
latency in the network and thus, imposes a constraint when
deploying the network in a setup with real time processing
requirements. In this paper, a Cost Function is computed as
the weighed sum of Energy conservation through
aggregation and latency incurred sue to aggregation. The
analytical model being developed is based on 250kbps, 2.4
GHz networks with AtMega128 microcontrollers [7] with
TinyOS. This cost function enable to set up threshold values
for data aggregation and latency tradeoffs so as to find
suitable design of WSN for specific network lifetime and
latency requirements of the deployment.
A. Motivation
Wireless sensor networks often consist of a large number of
low-cost sensor nodes that have strictly limited sensing,
computation, and communication capabilities. Due to
resource restricted sensor nodes, it is important to minimize
the amount of data transmission so that the average sensor
lifetime and the overall bandwidth utilization are improved.
Data aggregation is the process of summarizing and
combining sensor data in order to reduce the amount of data
transmission in the network. The cost of the aggregation is
related with the cost of aggregator which is proportional to
hardware/software capabilities. Thus, the cost of aggregator
An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks
(IJSRD/Vol. 2/Issue 08/2014/049)
All rights reserved by www.ijsrd.com 209
is related with the complexity of the aggregation operation.
However, data aggregation comes with the additional cost of
latency. Data from various nodes, if needs to be aggregated
at the aggregator, requires an additional time delay. This
makes the use of such networks unsuitable for deployment
for applications with soft and hard real time requirements.
However, setting up threshold limits for both aggregation
and latency, an optimal and economic design can be
computed for deployment in particular set-up. In this paper,
an analytical model has been developed to figure out the
tradeoff between data aggregation and latency, and can be
used to choose the type of deployment for particular
physical conditions.
B. Research Approach
Mathematical model of WSN node based on ATmega
microcontroller and TinyOS [8] operating system is being
developed. Power dissipation is computed using first order
radio energy dissipation model. Latency values are derived
from the time complexity of the algorithms needed to
aggregate the data at the cluster. It is assumed that LEACH
is used for clustering and hence the dissipation of energy is
almost identical for all the nodes for a complete duration of
one round. Finally, a Cost Function is developed as the
weighted sum of the energy savings due to clustering and
data aggregation and the time delay (Latency) incurred due
to aggregation is computed. The optimal value of the Cost
function can be taken as reference for deployment of WSN
in given physical conditions. Simulation of the model is
done using MATLAB, and simulation results are presented
along with the analytical results.
This paper is organized as follows. Section 1 gives
a brief overview of the paper. It discusses the scope and
motivation for the subject matter of the paper in a
comprehensive way. Section 2 provides the problem
statement and motivation.. Section 3 presents the analytical
model for the Energy dissipation using First order Radio
Electronics. It further derives equation for computation of
energy savings due to data aggregation and latency incurred
due to aggregation algorithms. Section 4 discusses the
results derived through analytical models and simulation
code. Section 5 discusses the conclusion and future scope of
the work.
III. PROPOSED WORK
A. Power Consumption Prospective: Data Aggregation and
Data Transmission
Energy consumption problem, being the most visible
challenge, is considered central to the sensor research
theme. The processing of data, memory accesses and
input/output operations, all consume sensor energy.
However, the major power drain occurs due to wireless
communication. Therefore, attempts require to be carried
out to perform as much in-network processing as possible
within a sensor or a group of sensors (cluster). This is
achieved by performing aggregation and filtration of raw
data before transmitting them to destined targets. As a result
of which redundancy in the recorded sensory samples is
eliminated, thereby reducing the transmission cost and
network overloading. Moreover, decrease in the effective
number of packet transmissions also leads to minimized
chances of network congestion, thereby saving the excess
energy consumption in the network, possibly at the expense
of latency in the operation. Moreover, computation at
intermediate node, possibly for calculation of average, mean
etc., requires additional logic circuitry leading to increased
cost and complexity.
The above facts can be explained using a simple
example with realistic values of quantities. If the radio
electronics requires 50nJ/bit and amplifier circuitry needs
10pJ/bit/m2
for communication, then power used in
transmitting 1 bit of information to the processing centre
situated 1 km away, consumes 1.005 × 104
nJ per unit time
(watts). However, energy used in data processing for
aggregation is 5nJ/bit/signal, which implies that execution
of almost 2010 instructions compensates the energy used for
one transmission in unit time. Therefore, it is quite
recommendable to apply aggregation techniques. Previous
researches have already proven the fact that in-network
processing cost is much less than the communication cost.
The proliferation of sensor network has created the urge of
exploring novel ideas for data aggregation. However, the
aggregation schemes would require efficient clustering
protocols to well-implement its functioning. Therefore,
efficient data aggregation techniques are to be used in
conjunction with efficient clustering protocols so as to give
optimum performance under given network constraints.
B. Radio Energy Dissipation Model
Radio energy consumption for TX and RX is measured based
on TinyOS 2.x. The computation of energy consumption for
compression and decompression is measured based on the
running time of each algorithm targeted at ATmega128L,
which is a micro-controller unit (MCU) used in many mote-
class sensor nodes. Total energy consumptions comprising
radio energy and CPU energy before and after applying
compression with respect to data size can be computed using
First order radio energy consumption model. Processing
delays, which eventually affect the end-to-end latency can
also be estimated.
Energy consumption before and after compression
in a simple topology consisting of sender and receiver can
be measured in a simple way. In a normal packet
transmission scenario, i.e., before-compression, radio energy
is consumed for transmitting the uncompressed data. On the
other hand, if we apply data compression, i.e., after-
compression, a sender transmits a packet with smaller data
length, reducing the radio energy. Since the packet length in
TinyOS is limited to 114 bytes, packets having larger than
such a limitation are split into multiple fragments.
Computational energy required for running the data
compression algorithm can be measured using standard
equations.
Fig. 3.1 A typical Transmitter and Receiver System
An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks
(IJSRD/Vol. 2/Issue 08/2014/049)
All rights reserved by www.ijsrd.com 210
1) Uncompressed Data transmitted from all nodes to Sink
Consider the following schematic diagram for illustration of
radio energy dissipation:
Fig 3.2 WSN without clustering and Data Aggregation
(Every node senses the data and transmits to sink)
The first order radio model is considered in with
ATMEGA parameter values are considered. The energy
spent in transmission of single bit to d distance is given by:
( )
where et1 is the energy dissipated per bit in the
transmitter circuitry and ed1dn
is the energy dissipated for
transmission of a single bit over a distance d, n being the
path loss exponent (usually 2.0≤n≤4.0). For simulation
purposes we have considered a first order model where we
assume n=2. Thus the total energy dissipated for
transmitting a K-bit packet is
( ) ( )
( )
where et=et1*K and ed= ed1*K. If er1 be the energy
required per bit for successful reception then energy
dissipated for receiving a K-bit packet is
( )
where, er=er1*K. In simulations model considered,
et1= 50 nJ/bit, ed1= 100 pJ/bit/m2
and er1= et1 with K = 2000
bits. It is assumed that the channel is symmetric so that the
energy spent in transmitting from node i to j is the same as
that of transmitting from node j to i for any Signal to Noise
Ratio. Let the mean number of packets transmitted to the
sink by each node assuming that all the nodes are
equidistant from the sink, is λ.
Considering Poisson Probability Distribution for
packets transmitted by each node to the sink, the following
equations can be derived.
( ) ( )
where λ is the mean number of data packets
transmitted by each node in the network in a unit time, R is
the number of bits per packet and N is the mean number of
transmitting nodes in the network.
Also,
( )
Thus, the total energy consumption in the network
is:
( ) ( )
It can be shown that total energy consumption for
TX and for the RX is less in case of compressed data as
compared with uncompressed data transmission. For a given
dataset, it can be shown that the total energy consumption
for transmission and reception with after-compression is less
than that of the normal packet transmission and reception,
i.e., before-compression. The amount of reduced radio
energy for transmitting and receiving packets with the
decreased data length due to compression is larger than the
amount of increased computation energy for compression in
TX and for decompression in RX.
For the two terms as stated in the above equations
on the RHS, the latter term denotes the amount of energy
dissipated in reception process. If all nodes are programmed
to transmit to the sink in the absence of any clustering, then
no intermediate node can receive the packet. Also, the sink
at which all the data packets arrives is plentiful and
abundant in all resources needed. Thus, without any loss of
generality in the specified configuration, the energy that
corresponds to data reception at the sink can be ignored as
that cannot affect network lifetime to any measurable extent.
2) Cluster Formation and Data Aggregation.
If the nodes in the network are suitably partitioned into
clusters of suitable size, then each cluster approximately
would have N/C nodes, where N is the mean number of
transmitting nodes in the network and C is the number of
clusters.
Consider the data aggregation at a cluster head.
Fig 3.3 Data Aggregation using Cluster Heads (Red Nodes
depicts Cluster Heads)
Consider the case of a single cluster. The energy
dissipation for data aggregation can be computed in a simple
way. ATmega128L used in this analysis is a 8 bit
microcontroller meaning that it processes 8 bits per cycle.
Assuming that is dissipates J joules per cycle, the cycles
needed to process bits is . However, the
number of CPU cycles needed to perform the analysis is
much more than this and depend upon the type of the
compression operation required in view of particular
deployment of the sensor network. Let S denotes the number
of CPU cycles needed to aggregate the data in the desired
way. Thus, S gives a measure of the complexity of the
process of data aggregation performed by the cluster head.
Then
( )
S gives the estimated number of cycles needed to
perform the aggregation by the Cluster Head (CH). T must
be chosen suitably so as to keep an upper limit on the
number of cycles needed to perform the computation,
provided a given data compression/aggregation algorithm is
provided.
The energy consumption in a cluster can be
computed in the following way. The energy consumed by
the cluster head in data aggregation is:
( )
An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks
(IJSRD/Vol. 2/Issue 08/2014/049)
All rights reserved by www.ijsrd.com 211
Here, J refers to the energy consumption per CPU
cycle. The mean number of packets arrived at the cluster
head per unit time is . However, aggregation
compresses the data and produce resultant data in a
compressed form. Assuming α be the compression factor,
the number of bits produced after compression is
The energy consumed in transmission by all nodes
of a cluster, to the CH, in some specific cluster is
( )
( )
Here d' denotes the distance of cluster head from
the nodes in the cluster. One simple assumption without the
loss of generality can be taken as d' = d/C where C is the
number of cluster heads. Also, energy consumption by the
CH in transferring a K bit aggregated packet to the sink is
( ) ( )
The term d' is considered again for the argument
stated above. The energy consumption at the sink is
( ) ( )
This term can be ignored as the energy
consumption at the sink is negligible as compared to the
resources available with it. Thus, the total energy
consumption per cluster in the said approach is
Energy consumption in complete network can be
obtained by multiplying with the number of clusters in the
network. Thus, the total energy consumption in the network
is
where C is the number of clusters.
Section 4 shows the results of the energy saving for data
operations for varying values of T.
3) Cluster Formation without Data Aggregation
Without data aggregation, the complete data in its entirety,
as received by the cluster heads from the individual nodes in
the cluster is to be transmitted to the sink.
Therefore, in this scenario,
where
( ) ( )
is defined with value of K having α =1, and .
Section 4 gives a relative comparison between each of the
three schemes of data transfer.
4) Data Aggregation and Latency
Latency gives a measure of delay incurred due to
intermediate processing at the cluster heads. Without data
aggregation, latency can be measured as the time complexity
of data aggregation at the sink, or simply,
( )
Where N is the number of nodes in the network and
O(N) refers to the Big O notation for an upper bound on the
given function of data aggregation.
Assuming a beacon oriented MAC layer
implementation, in a clustered network, the CH receives the
data packets from all the nodes almost simultaneously. The
time complexity of the aggregation operation is a function of
N/C. Thus
( )
However, the data from all the CH is again
aggregated at the sink, leading to a latency factor at the sink
itself.
( )
Data can be transferred in a clustered network
directly to the sink, without data aggregation. In this case,
the overall latency of network can be computed as the
latency in the uncluttered network, plus an additional
parameter to account for the TDMA schedule generated by
the cluster head and its corresponding transmission of the
data to the sink. Thus, for this configuration,
( )
where β is a constant to account for the
proportionate delay.
( )
Section 4 gives the analysis of the equation based
on the model of First order radio electronics power
dissipation and latency.
C. Optimal Number of Cluster for Given latency and
Energy Constraints
Clustering is the process of assigning a set of sensor nodes,
with similar attributes, to a specified group or cluster.
More number of nodes in any cluster signifies more
processing at CH, thereby increased battery life of the nodes
in the cluster but dissipation of more energy at CH in view
of processing of large volume of data. Thus, energy saving
in data transmission for nodes in the cluster will have an
adverse effect on the power consumption due to intense
processing at cluster head. Also, it leads to an increased
latency as the cluster head is a simple cluster and cannot
match the processing capabilities of full functional
processing machines like sink.
Less nodes per cluster means more clusters and
increased latency at sink due to data from multiple cluster
heads. However, it reduces latency at CH as low data in
view of small nodes per cluster needs to be processed.
However, an overall gain in terms of latency is obtained as
CH is one of the node elected as a leader through some
leader election algorithm (e.g. LEACH) whereas the sink
usually have much more computing power as compared to
the sensor node. Moreover, the energy consumption at the
CH is less in view of less CPU cycles needed to aggregate
the data.
It is assumed that formation of cluster follows a
procedure that includes the type of sensed data, geographical
positioning and distance between the nodes. Nevertheless,
clustering is almost unavoidable for a large class of sensor
networks. However, parametrically, certain analysis can be
drawn taking C (number of clusters) as the key parameter. In
the current analysis, a function is constructed as Cost
Function, and it is to be analyzed for varying values of C
and traffic conditions. The cost function is to be computed
as to give the overall performance value of the network
under specified parameters.
Consider the following equation for Cost Function:
An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks
(IJSRD/Vol. 2/Issue 08/2014/049)
All rights reserved by www.ijsrd.com 212
Here, u and v are suitable constants so as to match
the values and to emphasize on any one of the feature as the
case may be.
Section 4 analyzes the value of Cost Function
against standard values of model parameters and
conclusions are drawn.
IV. ANALYSIS OF PROPOSED WORK
A. Data Aggregation and Latency
For simulation purpose, the parameters used correspond to
TinyOS on ATmega128L microcontroller. Consider the
following maximum values of parameter:
Parameter Symbol and
Description.
Value
Data Rate, Frequency Band 250kbps, 2.4 GHz
Transmission Range 10m/20m
N; Number of Nodes in the
Network, considering all as the
active nodes
1000
C; Number of Clusters in the
Network
[0, 0.05]; Vector ranging
from 0 to 0.05 percent of
the number of nodes in
the cluster.
λ ; Mean number of Packets
transmitted by each node per
unit time
[0,280], corresponding to
250kbps data rate abd
114 byte packet size.
R; Bits per Packet
114*8 (TinyOS
Specification)
; energy dissipation per bit in
transmitter circuitry before
transmission
50nJ/bit
; energy dissipation per bit
in sending a bit over d distance.
10pJ/bit/m2
; energy dissipation per bit
in receiver circuitry.
50nJ/bit
J; Energy Consumption of
ATmega128L per CPU cycle.
Being 8 bit microcontroller, it
processes 8 bits per unit time.
100nJ/bit
d; mean distance of nodes from
the sink.
[1,20]
d' ; mean distance of cluster
heads from the sink
[1,20]/α; where α
depends upon the
particular deployment of
the sensor network.
Table 4.1parameter Specification Of Values Of Variables In
The Analytical Analysis
The following results are obtained corresponding to
the set of values as specified in table 4.1
Fig. 4.1 Power Consumption of a typical Transmitter
Receiver Pair as a function of number of packets transferred
and distance between the nodes.
Fig. 4.2 Power Consumption as a function of number of
packets transmitted and number of nodes (assuming distance
between the nodes is 10 mtrs).
Fig. 4.3 Power Consumption as a function of number of
packets transmitted and mean distance between nodes and
sink.
Fig. 4.4 Power Consumption as a function of number of
nodes and mean distance between nodes and sink.
Fig. 4.5 Power Consumption as a function of distance of
nodes from cluster heads and number of nodes.
An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks
(IJSRD/Vol. 2/Issue 08/2014/049)
All rights reserved by www.ijsrd.com 213
Fig. 4.6 Latency (Time Delay) in WSN with clustering but
without data aggregation
. Fig. 4.7 Power consumption in WSN as a function of data
aggregation (compression) and number of nodes.
Fig. 4.8 Latency of WSN as a function of Aggregation
factor and percentage of CHs
B. Calculation of Optimal Number of Clusters for Given
latency and Energy Constraints
Consider T be the upper bound on latency and E be the
upper bound on energy consumption. The cost function to
be minimized, as specified in section 3 is
Where
and
.
Analytical results can be derived to find the
optimal number of cluster head percentage for any Wireless
Sensor Network.
Table 4.3 can be derived from the parameter values
as specified in table 4.1 above:
Other parameter values considered are:
Parameter Value
α ; Compression Factor for Data; (1/ α)
Delay Factor in Latency Computation
0.1
AA Battery Life
AA: (2.85 Ah) x (1.5 V) x (3600 s) =
15,390 J
Number of Nodes 1000
Packet Transmission Rate
280 pkts per
second.
Table 4.2: Parameter Considered In Table 4.3
CH
proporti
on
C
H
N
C
H
power
consumptio
n
Life
Laten
cy
CF
0 0
10
00
25.57277 0
998.0
146
99.80
146
0.002 2
99
8
25.52183
29.12
261
998.0
073
128.9
233
0.004 4
99
6
25.47089
29.18
108
498.0
036
78.98
145
0.006 6
99
4
25.41994
29.23
98
331.3
357
62.37
337
0.008 8
99
2
25.369
29.29
875
248.0
018
54.09
893
0.01
1
0
99
0
25.31806
29.35
794
198.0
014
49.15
808
0.012
1
2
98
8
25.26711
29.41
737
164.6
679
45.88
416
0.014
1
4
98
6
25.21616
29.47
704
140.8
582
43.56
286
0.016
1
6
98
4
25.16522
29.53
695
123.0
009
41.83
704
0.018
1
8
98
2
25.11427
29.59
711
109.1
119
40.50
83
0.02
2
0
98
0
25.06332
29.65
751
98.00
071
39.45
758
0.022
2
2
97
8
25.01237
29.71
816
88.90
974
38.60
913
0.024
2
4
97
6
24.96142
29.77
906
81.33
393
37.91
245
0.026
2
6
97
4
24.91047
29.84
021
74.92
362
37.33
257
0.028
2
8
97
2
24.85951
29.90
161
69.42
908
36.84
451
0.03
3
0
97
0
24.80856
29.96
326
64.66
714
36.42
997
0.032
3
2
96
8
24.75761
30.02
517
60.50
044
36.07
521
0.034
3
4
96
6
24.70665
30.08
733
56.82
394
35.76
972
0.036
3
6
96
4
24.6557
30.14
975
53.55
595
35.50
535
0.038
3
8
96
2
24.60474
30.21
243
50.63
195
35.27
563
0.04
4
0
96
0
24.55378
30.27
538
48.00
035
35.07
541
0.042
4
2
95
8
24.50283
30.33
858
45.61
938
34.90
052
0.044
4
4
95
6
24.45187
30.40
205
43.45
486
34.74
754
An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks
(IJSRD/Vol. 2/Issue 08/2014/049)
All rights reserved by www.ijsrd.com 214
0.046
4
6
95
4
24.40091
30.46
579
41.47
856
34.61
364
0.048
4
8
95
2
24.34995
30.52
979
39.66
696
34.49
649
0.05
5
0
95
0
24.29899
30.59
406
38.00
028
34.39
409
0.052
5
2
94
8
24.24802
30.65
861
36.46
18
34.30
479
0.054
5
4
94
6
24.19706
30.72
343
35.03
729
34.22
715
0.056
5
6
94
4
24.1461
30.78
852
33.71
453
34.15
997
0.058
5
8
94
2
24.09513
30.85
389
32.48
3
34.10
219
0.06
6
0
94
0
24.04417
30.91
953
31.33
356
34.05
289
0.062
6
2
93
8
23.9932
30.98
546
30.25
829
34.01
129
0.064
6
4
93
6
23.94223
31.05
167
29.25
021
33.97
669
0.066
6
6
93
4
23.89127
31.11
816
28.30
324
33.94
848
0.068
6
8
93
2
23.8403
31.18
494
27.41
196
33.92
613
0.07
7
0
93
0
23.78933
31.25
2
26.57
162
33.90
916
0.072
7
2
92
8
23.73836
31.31
935
25.77
797
33.89
715
0.074
7
4
92
6
23.68739
31.38
7
25.02
721
33.88
972
0.076
7
6
92
4
23.63641
31.45
494
24.31
597
33.88
653
0.078
7
8
92
2
23.58544
31.52
317
23.64
12
33.88
729
0.08
8
0
92
0
23.53447
31.59
17
23.00
017
33.89
171
0.082
8
2
91
8
23.48349
31.66
052
22.39
041
33.89
956
0.084
8
4
91
6
23.43252
31.72
965
21.80
968
33.91
062
0.086
8
6
91
4
23.38154
31.79
908
21.25
597
33.92
468
0.088
8
8
91
2
23.33057
31.86
882
20.72
742
33.94
156
0.09
9
0
91
0
23.27959
31.93
886
20.22
237
33.96
109
0.092
9
2
90
8
23.22861
32.00
921
19.73
927
33.98
313
0.094
9
4
90
6
23.17763
32.07
987
19.27
674
34.00
754
0.096
9
6
90
4
23.12665
32.15
084
18.83
347
34.03
419
0.098
9
8
90
2
23.07567
32.22
213
18.40
83
34.06
296
0.1
1
0
0
90
0
23.02469
32.29
373
18.00
013
34.09
375
Table 4.3: Computation of Cost Function
Figure 4.9 Latency (time delay) plotted against the
proportion of cluster heads. Horizontal axis shows the
proportion of Cluster Heads while vertical axis shows the
latency.
Figure 4.10 Network life plotted against the proportion of
cluster heads. Horizontal axis shows the proportion of
Cluster Heads while vertical axis shows the network
lifetime.
Figure 4.11 Overall power consumption in the WSN.
Horizontal axis shows the proportion of Cluster Heads while
vertical axis shows the power consumption in joules
Figure 4.12 Cost Function plotted against cluster heads.
Horizontal axis shows the proportion of Cluster Heads while
vertical axis shows the value of cost function as the sum of
network lifetime and Latency.
Section 5 discusses the result of the paper and
outline the conclusion and future scope.
0
1000
2000
0
0.014
0.028
0.042
0.056
0.07
0.084
0.098
Latency
Latency
26
28
30
32
34
0
0.014
0.028
0.042
0.056
0.07
0.084
0.098
Network Life
Network
Life
10
11
12
13
0
0.016
0.032
0.048
0.064
0.08
0.096
Power
Consumption
power
consumpt
ion
33
33.5
34
34.5
35
35.5
0.038
0.048
0.058
0.068
0.078
0.088
0.098
Cost Function
Cost
Function
An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks
(IJSRD/Vol. 2/Issue 08/2014/049)
All rights reserved by www.ijsrd.com 215
V. CONCLUSION AND FUTURE SCOPE
This paper provides a detailed review of secure data
aggregation concept in wireless sensor networks Data
Aggregation is a technique for reducing the communication
overhead and optimizing the bandwidth utilization in the
wireless links.
To give the motivation behind secure data
aggregation, first, the energy dissipation computation of
wireless sensor networks are presented without clustering
and aggregation and the relationships between data
aggregation concept with energy consumption and latency
are explained. Second, an extensive literature survey is
presented by summarizing the state-of-the-art data
aggregation and clustering protocols. Based on this
extensive literature survey, open research areas and future
research directions are given.
However, data aggregation techniques raises
privacy and security issues of the sensor nodes which need
to share their data with the aggregator node. One of the most
critical issue raised by aggregation is latency or time delay
that occur due to the complexity of the aggregation function.
In this paper, a mathematical model is developed
for providing an analytical estimate to the increase in
network life due to reduction in in-network communication.
This is a direct consequence of data aggregation as it leads
to decrease in number of data packets for any transmission
between two nodes. This analysis is based on energy
dissipation model based on first order radio energy
dissipation. The same model is extended to include the delay
that incurred due to data aggregation algorithms executed at
the node. Moreover, the sensor node, acting as the
aggregator needs to wait for data packets from the entire
cluster before aggregation can actually be performed. This
supplements the latency in the network as a result of
clustering and data aggregation. Section 4 presents the
results for the mathematical expression derived for energy
consumption and latency tradeoff. It also derives the Cost
Function with energy dissipation and latency as parameter
values. The cost function is computed as the weighted sum
of network lifetime and latency and can be used as a
reference so as to setup a wireless sensor network under
given physical constraints of network lifetime and latency.
Computation of latency is becoming increasingly important
in conjunction with the hard and soft real time requirements
for almost all the classes of networks. Moreover, for human
life rated networks such a fire or radiation sensors, latency is
one of the most critical element.
A. Future Scope
As a future scope of this research, it is intended to consider a
stochastic model of the wireless sensor network under
prescribed network traffic condition to model the energy
dissipation more accurately as compared to presented in
current work. Also, a more accurate model of radio energy
dissipation is to be considered to account for more accurate
energy dissipation for a given class of hardware devices.
Implementation of a general class of aggregation functions
is to be computed for measurement of network life latency
tradeoffs.
REFERENCES
[1] Zhiyong Peng; Xiaojuan Li, "The improvement and
simulation of LEACH protocol for WSNs," Software
Engineering and Service Sciences (ICSESS), 2010
IEEE International Conference on , vol., no.,
pp.500,503, 16-18 July 2010 doi:
10.1109/ICSESS.2010.5552317
[2] Yuhua Liu; Jingju Gao; Longquan Zhu; Yugang
Zhang, "A Clustering Algorithm Based on
Communication Facility in WSN," Communications
and Mobile Computing, 2009. CMC '09. WRI
International Conference on , vol.2, no., pp.76,80, 6-8
Jan. 2009. doi: 10.1109/CMC.2009.236
[3] He, C.; Kiziroglou, Michail E.; Yates, D.C.;
Yeatman, E.M., "A MEMS Self-Powered Sensor and
RF Transmission Platform for WSN Nodes," Sensors
Journal, IEEE, vol.11, no.12, pp.3437,3445, Dec.
2011 doi: 10.1109/JSEN.2011.2160535.
[4] Van-Trinh Hoang; Julien, N.; Berruet, P., "Design
under constraints of availability and energy for sensor
node in wireless sensor network," Design and
Architectures for Signal and Image Processing
(DASIP), 2012 Conference on , vol., no., pp.1,8, 23-
25 Oct. 2012
[5] Khan, S.; Eui-Nam Huh; Rao, I, "Reliable Data
Dissemination with Diversity Multi-Hop Protocol for
Wireless Sensors Network," Advanced
Communication Technology, 2008. ICACT 2008.
10th International Conference on, vol.1, no., pp.9, 13,
17-20 Feb. 2008. doi: 10.1109/ICACT.2008.4493700
[6] Wen-Wen Huang; Ya-li Peng; Jian Wen; Min Yu,
"Energy-Efficient Multi-hop Hierarchical Routing
Protocol for Wireless Sensor Networks," Networks
Security, Wireless Communications and Trusted
Computing, 2009. NSWCTC '09. International
Conference on , vol.2, no., pp.469,472, 25-26 April
2009 doi: 10.1109/NSWCTC.2009.352
[7] Lihua Xiao; Qiong Liu, "A data fusion using un-even
clustering for WSN," Advanced Intelligence and
Awareness Internet (AIAI 2011), 2011 International
Conference on , vol., no., pp.216,219, 28-30 Oct.
2011. doi: 10.1049/cp.2011.1460
[8] Deosarkar, B.P.; Yadav, N.S.; Yadav, R. P.,
"Clusterhead selection in clustering algorithms for
wireless sensor networks: A survey," Computing,
Communication and Networking, 2008. ICCCn 2008.
International Conference on , vol., no., pp.1,8, 18-20
Dec. 2008 doi: 10.1109/ICCCNET.2008.4787686
[9] Aldalahmeh, S.; Ghogho, M., "Traffic estimation for
MAC protocols in distributed detection wireless
sensor networks," Signal Processing Conference
(EUSIPCO), 2012 Proceedings of the 20th European ,
vol., no., pp.719,723, 27-31 Aug. 2012
[10]Murillo, AF.; Pena, M.; Martinez, D., "Applications
of WSN in health and agriculture," Communications
Conference (COLCOM), 2012 IEEE Colombian ,
vol., no., pp.1,6, 16-18 May 2012 .doi:
10.1109/ColComCon.2012.6233678.
[11]Xinsheng Xia; Qilian Liang, "Latency-aware and
energy efficiency tradeoffs for wireless sensor
networks," Personal, Indoor and Mobile Radio
Communications, 2004. PIMRC 2004. 15th IEEE
An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks
(IJSRD/Vol. 2/Issue 08/2014/049)
All rights reserved by www.ijsrd.com 216
International Symposium on , vol.3, no.,
pp.1782,1786 Vol.3, 5-8 Sept. 2004.doi:
10.1109/PIMRC.2004.1368306
[12]Sivaraj, R.; Thangarajan, R., "Location and Time
Based Clone Detection in Wireless Sensor
Networks," Communication Systems and Network
Technologies (CSNT), 2014 Fourth International
Conference on , vol., no., pp.133,137, 7-9 April 2014
doi: 10.1109/CSNT.2014.
[13]El-Aaasser, M.; Ashour, M., "Energy aware
classification for wireless sensor networks routing,"
Advanced Communication Technology (ICACT),
2013 15th International Conference on , vol., no.,
pp.66,71, 27-30 Jan. 2013.
[14]Zhao Jun; Chen Xiang-guang; Xie Ying-xin, "The
application of multi-path fault tolerant algorithm in
WSN nodes," Artificial Intelligence, Management
Science and Electronic Commerce (AIMSEC), 2011
2nd International Conference on , vol., no.,
pp.7323,7326, 8-10 Aug. 2011 doi:
10.1109/AIMSEC.2011.6010112.
[15]Dhasian, H.R.; Balasubramanian, P., "Survey of data
aggregation techniques using soft computing in
wireless sensor networks," Information Security, IET
, vol.7, no.4, pp.336,342, December 2013 doi:
10.1049/iet-ifs.2012.0292
[16]Horvat, Goran; Zagar, Drago; Vinko, Davor,
"Influence of node deployment parameters on QoS in
large-scale WSN," Embedded Computing (MECO),
2014 3rd Mediterranean Conference on , vol., no.,
pp.202,205, 15-19 June 2014.
[17]Salman, N.; Rasool, I; Kemp, AH., "Overview of the
IEEE 802.15.4 standards family for Low Rate
Wireless Personal Area Networks," Wireless
Communication Systems (ISWCS), 2010 7th
International Symposium on , vol., no., pp.701,705,
19-22 Sept. 2010
doi: 10.1109/ISWCS.2010.5624516
[18]Ghosh, P.; Mayo, M.; Chaitankar, V.; Habib, T.;
Perkins, E.; Das, S.K., "Principles of genomic
robustness inspire fault-tolerant WSN topologies: A
network science based case study," Pervasive
Computing and Communications Workshops
(PERCOM Workshops), 2011 IEEE International
Conference on , vol., no., pp.160,165, 21-25 March
2011. doi: 10.1109/PERCOMW.2011.5766861
[19]Serfass, D.; Yoshigoe, K., "Wireless sensor networks
using Android Virtual Devices and Near Field
Communication peer-to-peer emulation,"
Southeastcon, 2013 Proceedings of IEEE , vol., no.,
pp.1,6, 4-7 April 2013. doi:
10.1109/SECON.2013.6567465.
[20]Chatterjee, A; Mukherjee, D., "Variety event
detection in Wireless Sensor Networks through single
hop cluster topology," Wireless and Optical
Communications Networks (WOCN), 2013 Tenth
International Conference on , vol., no., pp.1,5, 26-28
July 2013 doi: 10.1109/WOCN.2013.6616201
[21]Tianbo Wang; Wu, Chengdong; Peng Ji; Jian Zhang,
"A noble data aggregation algorithm for low latency
in wireless sensor network," Mechatronics and
Automation (ICMA), 2010 International Conference
on , vol., no., pp.894,898, 4-7 Aug. 2010
doi: 10.1109/ICMA.2010.5589006
[22]Chunyao Fui; Zhifang Jiangi; Wei Wei; Ang Wei, "
An Energy Balanced Algorithm of LEACH Protocol
in WSN ", IJCSI International Journal of Computer
Science Issues, Vol. 10, Issue 1, No 1, January 2013.

More Related Content

What's hot

Data Centric Approach Based Protocol using Evolutionary Approach in WSN
Data Centric Approach Based Protocol using Evolutionary Approach in WSNData Centric Approach Based Protocol using Evolutionary Approach in WSN
Data Centric Approach Based Protocol using Evolutionary Approach in WSNijsrd.com
 
Time Orient Multi Attribute Sensor Selection Technique For Data Collection In...
Time Orient Multi Attribute Sensor Selection Technique For Data Collection In...Time Orient Multi Attribute Sensor Selection Technique For Data Collection In...
Time Orient Multi Attribute Sensor Selection Technique For Data Collection In...inventionjournals
 
Security based Clock Synchronization technique in Wireless Sensor Network for...
Security based Clock Synchronization technique in Wireless Sensor Network for...Security based Clock Synchronization technique in Wireless Sensor Network for...
Security based Clock Synchronization technique in Wireless Sensor Network for...iosrjce
 
Bidirectional data centric routing protocol to improve the energy efficiency ...
Bidirectional data centric routing protocol to improve the energy efficiency ...Bidirectional data centric routing protocol to improve the energy efficiency ...
Bidirectional data centric routing protocol to improve the energy efficiency ...prj_publication
 
Ijarcet vol-2-issue-3-916-919
Ijarcet vol-2-issue-3-916-919Ijarcet vol-2-issue-3-916-919
Ijarcet vol-2-issue-3-916-919Editor IJARCET
 
A COST EFFECTIVE COMPRESSIVE DATA AGGREGATION TECHNIQUE FOR WIRELESS SENSOR N...
A COST EFFECTIVE COMPRESSIVE DATA AGGREGATION TECHNIQUE FOR WIRELESS SENSOR N...A COST EFFECTIVE COMPRESSIVE DATA AGGREGATION TECHNIQUE FOR WIRELESS SENSOR N...
A COST EFFECTIVE COMPRESSIVE DATA AGGREGATION TECHNIQUE FOR WIRELESS SENSOR N...ijasuc
 
DESIGNING SECURE CLUSTERING PROTOCOL WITH THE APPROACH OF REDUCING ENERGY CON...
DESIGNING SECURE CLUSTERING PROTOCOL WITH THE APPROACH OF REDUCING ENERGY CON...DESIGNING SECURE CLUSTERING PROTOCOL WITH THE APPROACH OF REDUCING ENERGY CON...
DESIGNING SECURE CLUSTERING PROTOCOL WITH THE APPROACH OF REDUCING ENERGY CON...IJCNCJournal
 
Energy Consumption Minimization in WSN using BFO
 Energy Consumption Minimization in WSN using BFO Energy Consumption Minimization in WSN using BFO
Energy Consumption Minimization in WSN using BFOrahulmonikasharma
 
Ant Colony Optimization for Wireless Sensor Network: A Review
Ant Colony Optimization for Wireless Sensor Network: A ReviewAnt Colony Optimization for Wireless Sensor Network: A Review
Ant Colony Optimization for Wireless Sensor Network: A Reviewiosrjce
 
Presentation on sensor network
Presentation on sensor networkPresentation on sensor network
Presentation on sensor networksarathanjabam
 
ENERGY EFFICIENT, LIFETIME IMPROVING AND SECURE PERIODIC DATA COLLECTION PROT...
ENERGY EFFICIENT, LIFETIME IMPROVING AND SECURE PERIODIC DATA COLLECTION PROT...ENERGY EFFICIENT, LIFETIME IMPROVING AND SECURE PERIODIC DATA COLLECTION PROT...
ENERGY EFFICIENT, LIFETIME IMPROVING AND SECURE PERIODIC DATA COLLECTION PROT...ijcsa
 
Data aggregation in wireless sensor network , 11751 d5811
Data aggregation in wireless sensor network , 11751 d5811Data aggregation in wireless sensor network , 11751 d5811
Data aggregation in wireless sensor network , 11751 d5811praveen369
 
WIRELESS SENSOR NETWORK CLUSTERING USING PARTICLES SWARM OPTIMIZATION FOR RED...
WIRELESS SENSOR NETWORK CLUSTERING USING PARTICLES SWARM OPTIMIZATION FOR RED...WIRELESS SENSOR NETWORK CLUSTERING USING PARTICLES SWARM OPTIMIZATION FOR RED...
WIRELESS SENSOR NETWORK CLUSTERING USING PARTICLES SWARM OPTIMIZATION FOR RED...IJMIT JOURNAL
 
Communication Cost Reduction by Data Aggregation: A Survey
Communication Cost Reduction by Data Aggregation: A SurveyCommunication Cost Reduction by Data Aggregation: A Survey
Communication Cost Reduction by Data Aggregation: A SurveyIJMTST Journal
 
A NOVEL APPROACH FOR ENERGY EFFICIENT HIERARCHY BASED ROUTING IN SENSOR NETWO...
A NOVEL APPROACH FOR ENERGY EFFICIENT HIERARCHY BASED ROUTING IN SENSOR NETWO...A NOVEL APPROACH FOR ENERGY EFFICIENT HIERARCHY BASED ROUTING IN SENSOR NETWO...
A NOVEL APPROACH FOR ENERGY EFFICIENT HIERARCHY BASED ROUTING IN SENSOR NETWO...cscpconf
 
Performance evaluation of data filtering approach in wireless sensor networks...
Performance evaluation of data filtering approach in wireless sensor networks...Performance evaluation of data filtering approach in wireless sensor networks...
Performance evaluation of data filtering approach in wireless sensor networks...ijmnct
 
Iaetsd a survey on enroute filtering scheme in
Iaetsd a survey on enroute filtering scheme inIaetsd a survey on enroute filtering scheme in
Iaetsd a survey on enroute filtering scheme inIaetsd Iaetsd
 
SENSOR NETWORKS BASED ON DISTRIBUTED WIRELESS SENSING
SENSOR NETWORKS BASED ON DISTRIBUTED WIRELESS SENSINGSENSOR NETWORKS BASED ON DISTRIBUTED WIRELESS SENSING
SENSOR NETWORKS BASED ON DISTRIBUTED WIRELESS SENSINGIJARIDEA Journal
 
CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...
CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...
CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...ijwmn
 

What's hot (20)

Data Centric Approach Based Protocol using Evolutionary Approach in WSN
Data Centric Approach Based Protocol using Evolutionary Approach in WSNData Centric Approach Based Protocol using Evolutionary Approach in WSN
Data Centric Approach Based Protocol using Evolutionary Approach in WSN
 
Time Orient Multi Attribute Sensor Selection Technique For Data Collection In...
Time Orient Multi Attribute Sensor Selection Technique For Data Collection In...Time Orient Multi Attribute Sensor Selection Technique For Data Collection In...
Time Orient Multi Attribute Sensor Selection Technique For Data Collection In...
 
Security based Clock Synchronization technique in Wireless Sensor Network for...
Security based Clock Synchronization technique in Wireless Sensor Network for...Security based Clock Synchronization technique in Wireless Sensor Network for...
Security based Clock Synchronization technique in Wireless Sensor Network for...
 
Bidirectional data centric routing protocol to improve the energy efficiency ...
Bidirectional data centric routing protocol to improve the energy efficiency ...Bidirectional data centric routing protocol to improve the energy efficiency ...
Bidirectional data centric routing protocol to improve the energy efficiency ...
 
Ijarcet vol-2-issue-3-916-919
Ijarcet vol-2-issue-3-916-919Ijarcet vol-2-issue-3-916-919
Ijarcet vol-2-issue-3-916-919
 
A COST EFFECTIVE COMPRESSIVE DATA AGGREGATION TECHNIQUE FOR WIRELESS SENSOR N...
A COST EFFECTIVE COMPRESSIVE DATA AGGREGATION TECHNIQUE FOR WIRELESS SENSOR N...A COST EFFECTIVE COMPRESSIVE DATA AGGREGATION TECHNIQUE FOR WIRELESS SENSOR N...
A COST EFFECTIVE COMPRESSIVE DATA AGGREGATION TECHNIQUE FOR WIRELESS SENSOR N...
 
DESIGNING SECURE CLUSTERING PROTOCOL WITH THE APPROACH OF REDUCING ENERGY CON...
DESIGNING SECURE CLUSTERING PROTOCOL WITH THE APPROACH OF REDUCING ENERGY CON...DESIGNING SECURE CLUSTERING PROTOCOL WITH THE APPROACH OF REDUCING ENERGY CON...
DESIGNING SECURE CLUSTERING PROTOCOL WITH THE APPROACH OF REDUCING ENERGY CON...
 
Energy Consumption Minimization in WSN using BFO
 Energy Consumption Minimization in WSN using BFO Energy Consumption Minimization in WSN using BFO
Energy Consumption Minimization in WSN using BFO
 
Ant Colony Optimization for Wireless Sensor Network: A Review
Ant Colony Optimization for Wireless Sensor Network: A ReviewAnt Colony Optimization for Wireless Sensor Network: A Review
Ant Colony Optimization for Wireless Sensor Network: A Review
 
Presentation on sensor network
Presentation on sensor networkPresentation on sensor network
Presentation on sensor network
 
ENERGY EFFICIENT, LIFETIME IMPROVING AND SECURE PERIODIC DATA COLLECTION PROT...
ENERGY EFFICIENT, LIFETIME IMPROVING AND SECURE PERIODIC DATA COLLECTION PROT...ENERGY EFFICIENT, LIFETIME IMPROVING AND SECURE PERIODIC DATA COLLECTION PROT...
ENERGY EFFICIENT, LIFETIME IMPROVING AND SECURE PERIODIC DATA COLLECTION PROT...
 
Dy4301752755
Dy4301752755Dy4301752755
Dy4301752755
 
Data aggregation in wireless sensor network , 11751 d5811
Data aggregation in wireless sensor network , 11751 d5811Data aggregation in wireless sensor network , 11751 d5811
Data aggregation in wireless sensor network , 11751 d5811
 
WIRELESS SENSOR NETWORK CLUSTERING USING PARTICLES SWARM OPTIMIZATION FOR RED...
WIRELESS SENSOR NETWORK CLUSTERING USING PARTICLES SWARM OPTIMIZATION FOR RED...WIRELESS SENSOR NETWORK CLUSTERING USING PARTICLES SWARM OPTIMIZATION FOR RED...
WIRELESS SENSOR NETWORK CLUSTERING USING PARTICLES SWARM OPTIMIZATION FOR RED...
 
Communication Cost Reduction by Data Aggregation: A Survey
Communication Cost Reduction by Data Aggregation: A SurveyCommunication Cost Reduction by Data Aggregation: A Survey
Communication Cost Reduction by Data Aggregation: A Survey
 
A NOVEL APPROACH FOR ENERGY EFFICIENT HIERARCHY BASED ROUTING IN SENSOR NETWO...
A NOVEL APPROACH FOR ENERGY EFFICIENT HIERARCHY BASED ROUTING IN SENSOR NETWO...A NOVEL APPROACH FOR ENERGY EFFICIENT HIERARCHY BASED ROUTING IN SENSOR NETWO...
A NOVEL APPROACH FOR ENERGY EFFICIENT HIERARCHY BASED ROUTING IN SENSOR NETWO...
 
Performance evaluation of data filtering approach in wireless sensor networks...
Performance evaluation of data filtering approach in wireless sensor networks...Performance evaluation of data filtering approach in wireless sensor networks...
Performance evaluation of data filtering approach in wireless sensor networks...
 
Iaetsd a survey on enroute filtering scheme in
Iaetsd a survey on enroute filtering scheme inIaetsd a survey on enroute filtering scheme in
Iaetsd a survey on enroute filtering scheme in
 
SENSOR NETWORKS BASED ON DISTRIBUTED WIRELESS SENSING
SENSOR NETWORKS BASED ON DISTRIBUTED WIRELESS SENSINGSENSOR NETWORKS BASED ON DISTRIBUTED WIRELESS SENSING
SENSOR NETWORKS BASED ON DISTRIBUTED WIRELESS SENSING
 
CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...
CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...
CUTTING DOWN ENERGY USAGE IN WIRELESS SENSOR NETWORKS USING DUTY CYCLE TECHNI...
 

Viewers also liked

Implementing ieee 802 7 ccc
Implementing ieee 802 7 cccImplementing ieee 802 7 ccc
Implementing ieee 802 7 cccgmeneses23
 
Learning and imitation in heterogeneous robot groups
Learning and imitation in heterogeneous robot groupsLearning and imitation in heterogeneous robot groups
Learning and imitation in heterogeneous robot groupsWilli Richert
 
Simulation Based Routing Protocols Evaluation for IEEE 802.15.4 enabled Wirel...
Simulation Based Routing Protocols Evaluation for IEEE 802.15.4 enabled Wirel...Simulation Based Routing Protocols Evaluation for IEEE 802.15.4 enabled Wirel...
Simulation Based Routing Protocols Evaluation for IEEE 802.15.4 enabled Wirel...IDES Editor
 
Precision agriculture
Precision agriculturePrecision agriculture
Precision agricultureHenk Hogeveen
 
Wireless Personal Area Networks (WPAN): Lowrate amd High Rate
Wireless Personal Area Networks (WPAN): Lowrate amd High RateWireless Personal Area Networks (WPAN): Lowrate amd High Rate
Wireless Personal Area Networks (WPAN): Lowrate amd High RateDon Norwood
 
Role of computers in science and technology agriculture
Role of computers in science and technology agricultureRole of computers in science and technology agriculture
Role of computers in science and technology agricultureGobind Raj Aulakh
 
Personal area network (pan)
Personal area network (pan)Personal area network (pan)
Personal area network (pan)Kukuh Rahmadi
 
climate change and its effect on agriculture
climate change and its effect on agricultureclimate change and its effect on agriculture
climate change and its effect on agriculturemohini singh
 
CS6003 AD HOC AND SENSOR NETWORKS
CS6003 AD HOC AND SENSOR NETWORKSCS6003 AD HOC AND SENSOR NETWORKS
CS6003 AD HOC AND SENSOR NETWORKSKathirvel Ayyaswamy
 

Viewers also liked (13)

Implementing ieee 802 7 ccc
Implementing ieee 802 7 cccImplementing ieee 802 7 ccc
Implementing ieee 802 7 ccc
 
Learning and imitation in heterogeneous robot groups
Learning and imitation in heterogeneous robot groupsLearning and imitation in heterogeneous robot groups
Learning and imitation in heterogeneous robot groups
 
Simulation Based Routing Protocols Evaluation for IEEE 802.15.4 enabled Wirel...
Simulation Based Routing Protocols Evaluation for IEEE 802.15.4 enabled Wirel...Simulation Based Routing Protocols Evaluation for IEEE 802.15.4 enabled Wirel...
Simulation Based Routing Protocols Evaluation for IEEE 802.15.4 enabled Wirel...
 
WSNs & Agriculture
WSNs & AgricultureWSNs & Agriculture
WSNs & Agriculture
 
Aahb workshop
Aahb workshopAahb workshop
Aahb workshop
 
Precision agriculture
Precision agriculturePrecision agriculture
Precision agriculture
 
Wireless Personal Area Networks (WPAN): Lowrate amd High Rate
Wireless Personal Area Networks (WPAN): Lowrate amd High RateWireless Personal Area Networks (WPAN): Lowrate amd High Rate
Wireless Personal Area Networks (WPAN): Lowrate amd High Rate
 
Precision agriculture
Precision agriculturePrecision agriculture
Precision agriculture
 
Role of computers in science and technology agriculture
Role of computers in science and technology agricultureRole of computers in science and technology agriculture
Role of computers in science and technology agriculture
 
Personal area network (pan)
Personal area network (pan)Personal area network (pan)
Personal area network (pan)
 
Personal area networks (PAN)
Personal area networks (PAN)Personal area networks (PAN)
Personal area networks (PAN)
 
climate change and its effect on agriculture
climate change and its effect on agricultureclimate change and its effect on agriculture
climate change and its effect on agriculture
 
CS6003 AD HOC AND SENSOR NETWORKS
CS6003 AD HOC AND SENSOR NETWORKSCS6003 AD HOC AND SENSOR NETWORKS
CS6003 AD HOC AND SENSOR NETWORKS
 

Similar to An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks

Protected Data Collection In WSN by Filtering Attackers Influence (Published ...
Protected Data Collection In WSN by Filtering Attackers Influence (Published ...Protected Data Collection In WSN by Filtering Attackers Influence (Published ...
Protected Data Collection In WSN by Filtering Attackers Influence (Published ...sangasandeep
 
Data gathering in wireless sensor networks using intermediate nodes
Data gathering in wireless sensor networks using intermediate nodesData gathering in wireless sensor networks using intermediate nodes
Data gathering in wireless sensor networks using intermediate nodesIJCNCJournal
 
A Reliable Routing Technique for Wireless Sensor Networks
A Reliable Routing Technique for Wireless Sensor NetworksA Reliable Routing Technique for Wireless Sensor Networks
A Reliable Routing Technique for Wireless Sensor NetworksEditor IJCATR
 
CONCEALED DATA AGGREGATION WITH DYNAMIC INTRUSION DETECTION SYSTEM TO REMOVE ...
CONCEALED DATA AGGREGATION WITH DYNAMIC INTRUSION DETECTION SYSTEM TO REMOVE ...CONCEALED DATA AGGREGATION WITH DYNAMIC INTRUSION DETECTION SYSTEM TO REMOVE ...
CONCEALED DATA AGGREGATION WITH DYNAMIC INTRUSION DETECTION SYSTEM TO REMOVE ...cscpconf
 
Concealed Data Aggregation with Dynamic Intrusion Detection System to Remove ...
Concealed Data Aggregation with Dynamic Intrusion Detection System to Remove ...Concealed Data Aggregation with Dynamic Intrusion Detection System to Remove ...
Concealed Data Aggregation with Dynamic Intrusion Detection System to Remove ...csandit
 
A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wir...
A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wir...A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wir...
A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wir...Editor IJCATR
 
Scalable and Robust Hierarchical Group of Data in Wireless Sensor Networks
Scalable and Robust Hierarchical Group of Data in Wireless Sensor NetworksScalable and Robust Hierarchical Group of Data in Wireless Sensor Networks
Scalable and Robust Hierarchical Group of Data in Wireless Sensor NetworksIJERA Editor
 
Mobile Cloud Technology And Technology
Mobile Cloud Technology And TechnologyMobile Cloud Technology And Technology
Mobile Cloud Technology And TechnologyMelissa Buckley
 
Secure authentication and data aggregation scheme for routing packets in wire...
Secure authentication and data aggregation scheme for routing packets in wire...Secure authentication and data aggregation scheme for routing packets in wire...
Secure authentication and data aggregation scheme for routing packets in wire...IJECEIAES
 
IRJET-Structure less Efficient Data Aggregation and Data Integrity in Sensor ...
IRJET-Structure less Efficient Data Aggregation and Data Integrity in Sensor ...IRJET-Structure less Efficient Data Aggregation and Data Integrity in Sensor ...
IRJET-Structure less Efficient Data Aggregation and Data Integrity in Sensor ...IRJET Journal
 
Secure and Efficient Hierarchical Data Aggregation in Wireless Sensor Networks
Secure and Efficient Hierarchical Data Aggregation in Wireless Sensor NetworksSecure and Efficient Hierarchical Data Aggregation in Wireless Sensor Networks
Secure and Efficient Hierarchical Data Aggregation in Wireless Sensor NetworksIJMER
 
Ijarcet vol-2-issue-2-756-760
Ijarcet vol-2-issue-2-756-760Ijarcet vol-2-issue-2-756-760
Ijarcet vol-2-issue-2-756-760Editor IJARCET
 
Ijarcet vol-2-issue-2-576-581
Ijarcet vol-2-issue-2-576-581Ijarcet vol-2-issue-2-576-581
Ijarcet vol-2-issue-2-576-581Editor IJARCET
 
Energy Proficient and Security Protocol for WSN: A Review
Energy Proficient and Security Protocol for WSN: A ReviewEnergy Proficient and Security Protocol for WSN: A Review
Energy Proficient and Security Protocol for WSN: A Reviewtheijes
 
J031101064069
J031101064069J031101064069
J031101064069theijes
 
CONSENSUS BASED DATA AGGREGATION FOR ENERGY CONSERVATION IN WIRELESS SENSOR N...
CONSENSUS BASED DATA AGGREGATION FOR ENERGY CONSERVATION IN WIRELESS SENSOR N...CONSENSUS BASED DATA AGGREGATION FOR ENERGY CONSERVATION IN WIRELESS SENSOR N...
CONSENSUS BASED DATA AGGREGATION FOR ENERGY CONSERVATION IN WIRELESS SENSOR N...ijdpsjournal
 
CONSENSUS BASED DATA AGGREGATION FOR ENERGY CONSERVATION IN WIRELESS SENSOR ...
CONSENSUS BASED DATA AGGREGATION FOR  ENERGY CONSERVATION IN WIRELESS SENSOR ...CONSENSUS BASED DATA AGGREGATION FOR  ENERGY CONSERVATION IN WIRELESS SENSOR ...
CONSENSUS BASED DATA AGGREGATION FOR ENERGY CONSERVATION IN WIRELESS SENSOR ...ijdpsjournal
 
ALL ABOUT DATA AGGREGATION IN WIRELESS SENSOR NETWORKS
ALL ABOUT DATA AGGREGATION IN WIRELESS SENSOR NETWORKSALL ABOUT DATA AGGREGATION IN WIRELESS SENSOR NETWORKS
ALL ABOUT DATA AGGREGATION IN WIRELESS SENSOR NETWORKSEditor IJCTER
 
Data Analysis In The Cloud
Data Analysis In The CloudData Analysis In The Cloud
Data Analysis In The CloudMonica Carter
 

Similar to An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks (20)

Protected Data Collection In WSN by Filtering Attackers Influence (Published ...
Protected Data Collection In WSN by Filtering Attackers Influence (Published ...Protected Data Collection In WSN by Filtering Attackers Influence (Published ...
Protected Data Collection In WSN by Filtering Attackers Influence (Published ...
 
Data gathering in wireless sensor networks using intermediate nodes
Data gathering in wireless sensor networks using intermediate nodesData gathering in wireless sensor networks using intermediate nodes
Data gathering in wireless sensor networks using intermediate nodes
 
A Reliable Routing Technique for Wireless Sensor Networks
A Reliable Routing Technique for Wireless Sensor NetworksA Reliable Routing Technique for Wireless Sensor Networks
A Reliable Routing Technique for Wireless Sensor Networks
 
CONCEALED DATA AGGREGATION WITH DYNAMIC INTRUSION DETECTION SYSTEM TO REMOVE ...
CONCEALED DATA AGGREGATION WITH DYNAMIC INTRUSION DETECTION SYSTEM TO REMOVE ...CONCEALED DATA AGGREGATION WITH DYNAMIC INTRUSION DETECTION SYSTEM TO REMOVE ...
CONCEALED DATA AGGREGATION WITH DYNAMIC INTRUSION DETECTION SYSTEM TO REMOVE ...
 
Concealed Data Aggregation with Dynamic Intrusion Detection System to Remove ...
Concealed Data Aggregation with Dynamic Intrusion Detection System to Remove ...Concealed Data Aggregation with Dynamic Intrusion Detection System to Remove ...
Concealed Data Aggregation with Dynamic Intrusion Detection System to Remove ...
 
A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wir...
A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wir...A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wir...
A Fault tolerant system based on Genetic Algorithm for Target Tracking in Wir...
 
C018141418
C018141418C018141418
C018141418
 
Scalable and Robust Hierarchical Group of Data in Wireless Sensor Networks
Scalable and Robust Hierarchical Group of Data in Wireless Sensor NetworksScalable and Robust Hierarchical Group of Data in Wireless Sensor Networks
Scalable and Robust Hierarchical Group of Data in Wireless Sensor Networks
 
Mobile Cloud Technology And Technology
Mobile Cloud Technology And TechnologyMobile Cloud Technology And Technology
Mobile Cloud Technology And Technology
 
Secure authentication and data aggregation scheme for routing packets in wire...
Secure authentication and data aggregation scheme for routing packets in wire...Secure authentication and data aggregation scheme for routing packets in wire...
Secure authentication and data aggregation scheme for routing packets in wire...
 
IRJET-Structure less Efficient Data Aggregation and Data Integrity in Sensor ...
IRJET-Structure less Efficient Data Aggregation and Data Integrity in Sensor ...IRJET-Structure less Efficient Data Aggregation and Data Integrity in Sensor ...
IRJET-Structure less Efficient Data Aggregation and Data Integrity in Sensor ...
 
Secure and Efficient Hierarchical Data Aggregation in Wireless Sensor Networks
Secure and Efficient Hierarchical Data Aggregation in Wireless Sensor NetworksSecure and Efficient Hierarchical Data Aggregation in Wireless Sensor Networks
Secure and Efficient Hierarchical Data Aggregation in Wireless Sensor Networks
 
Ijarcet vol-2-issue-2-756-760
Ijarcet vol-2-issue-2-756-760Ijarcet vol-2-issue-2-756-760
Ijarcet vol-2-issue-2-756-760
 
Ijarcet vol-2-issue-2-576-581
Ijarcet vol-2-issue-2-576-581Ijarcet vol-2-issue-2-576-581
Ijarcet vol-2-issue-2-576-581
 
Energy Proficient and Security Protocol for WSN: A Review
Energy Proficient and Security Protocol for WSN: A ReviewEnergy Proficient and Security Protocol for WSN: A Review
Energy Proficient and Security Protocol for WSN: A Review
 
J031101064069
J031101064069J031101064069
J031101064069
 
CONSENSUS BASED DATA AGGREGATION FOR ENERGY CONSERVATION IN WIRELESS SENSOR N...
CONSENSUS BASED DATA AGGREGATION FOR ENERGY CONSERVATION IN WIRELESS SENSOR N...CONSENSUS BASED DATA AGGREGATION FOR ENERGY CONSERVATION IN WIRELESS SENSOR N...
CONSENSUS BASED DATA AGGREGATION FOR ENERGY CONSERVATION IN WIRELESS SENSOR N...
 
CONSENSUS BASED DATA AGGREGATION FOR ENERGY CONSERVATION IN WIRELESS SENSOR ...
CONSENSUS BASED DATA AGGREGATION FOR  ENERGY CONSERVATION IN WIRELESS SENSOR ...CONSENSUS BASED DATA AGGREGATION FOR  ENERGY CONSERVATION IN WIRELESS SENSOR ...
CONSENSUS BASED DATA AGGREGATION FOR ENERGY CONSERVATION IN WIRELESS SENSOR ...
 
ALL ABOUT DATA AGGREGATION IN WIRELESS SENSOR NETWORKS
ALL ABOUT DATA AGGREGATION IN WIRELESS SENSOR NETWORKSALL ABOUT DATA AGGREGATION IN WIRELESS SENSOR NETWORKS
ALL ABOUT DATA AGGREGATION IN WIRELESS SENSOR NETWORKS
 
Data Analysis In The Cloud
Data Analysis In The CloudData Analysis In The Cloud
Data Analysis In The Cloud
 

More from ijsrd.com

IoT Enabled Smart Grid
IoT Enabled Smart GridIoT Enabled Smart Grid
IoT Enabled Smart Gridijsrd.com
 
A Survey Report on : Security & Challenges in Internet of Things
A Survey Report on : Security & Challenges in Internet of ThingsA Survey Report on : Security & Challenges in Internet of Things
A Survey Report on : Security & Challenges in Internet of Thingsijsrd.com
 
IoT for Everyday Life
IoT for Everyday LifeIoT for Everyday Life
IoT for Everyday Lifeijsrd.com
 
Study on Issues in Managing and Protecting Data of IOT
Study on Issues in Managing and Protecting Data of IOTStudy on Issues in Managing and Protecting Data of IOT
Study on Issues in Managing and Protecting Data of IOTijsrd.com
 
Interactive Technologies for Improving Quality of Education to Build Collabor...
Interactive Technologies for Improving Quality of Education to Build Collabor...Interactive Technologies for Improving Quality of Education to Build Collabor...
Interactive Technologies for Improving Quality of Education to Build Collabor...ijsrd.com
 
Internet of Things - Paradigm Shift of Future Internet Application for Specia...
Internet of Things - Paradigm Shift of Future Internet Application for Specia...Internet of Things - Paradigm Shift of Future Internet Application for Specia...
Internet of Things - Paradigm Shift of Future Internet Application for Specia...ijsrd.com
 
A Study of the Adverse Effects of IoT on Student's Life
A Study of the Adverse Effects of IoT on Student's LifeA Study of the Adverse Effects of IoT on Student's Life
A Study of the Adverse Effects of IoT on Student's Lifeijsrd.com
 
Pedagogy for Effective use of ICT in English Language Learning
Pedagogy for Effective use of ICT in English Language LearningPedagogy for Effective use of ICT in English Language Learning
Pedagogy for Effective use of ICT in English Language Learningijsrd.com
 
Virtual Eye - Smart Traffic Navigation System
Virtual Eye - Smart Traffic Navigation SystemVirtual Eye - Smart Traffic Navigation System
Virtual Eye - Smart Traffic Navigation Systemijsrd.com
 
Ontological Model of Educational Programs in Computer Science (Bachelor and M...
Ontological Model of Educational Programs in Computer Science (Bachelor and M...Ontological Model of Educational Programs in Computer Science (Bachelor and M...
Ontological Model of Educational Programs in Computer Science (Bachelor and M...ijsrd.com
 
Understanding IoT Management for Smart Refrigerator
Understanding IoT Management for Smart RefrigeratorUnderstanding IoT Management for Smart Refrigerator
Understanding IoT Management for Smart Refrigeratorijsrd.com
 
DESIGN AND ANALYSIS OF DOUBLE WISHBONE SUSPENSION SYSTEM USING FINITE ELEMENT...
DESIGN AND ANALYSIS OF DOUBLE WISHBONE SUSPENSION SYSTEM USING FINITE ELEMENT...DESIGN AND ANALYSIS OF DOUBLE WISHBONE SUSPENSION SYSTEM USING FINITE ELEMENT...
DESIGN AND ANALYSIS OF DOUBLE WISHBONE SUSPENSION SYSTEM USING FINITE ELEMENT...ijsrd.com
 
A Review: Microwave Energy for materials processing
A Review: Microwave Energy for materials processingA Review: Microwave Energy for materials processing
A Review: Microwave Energy for materials processingijsrd.com
 
Web Usage Mining: A Survey on User's Navigation Pattern from Web Logs
Web Usage Mining: A Survey on User's Navigation Pattern from Web LogsWeb Usage Mining: A Survey on User's Navigation Pattern from Web Logs
Web Usage Mining: A Survey on User's Navigation Pattern from Web Logsijsrd.com
 
APPLICATION OF STATCOM to IMPROVED DYNAMIC PERFORMANCE OF POWER SYSTEM
APPLICATION OF STATCOM to IMPROVED DYNAMIC PERFORMANCE OF POWER SYSTEMAPPLICATION OF STATCOM to IMPROVED DYNAMIC PERFORMANCE OF POWER SYSTEM
APPLICATION OF STATCOM to IMPROVED DYNAMIC PERFORMANCE OF POWER SYSTEMijsrd.com
 
Making model of dual axis solar tracking with Maximum Power Point Tracking
Making model of dual axis solar tracking with Maximum Power Point TrackingMaking model of dual axis solar tracking with Maximum Power Point Tracking
Making model of dual axis solar tracking with Maximum Power Point Trackingijsrd.com
 
A REVIEW PAPER ON PERFORMANCE AND EMISSION TEST OF 4 STROKE DIESEL ENGINE USI...
A REVIEW PAPER ON PERFORMANCE AND EMISSION TEST OF 4 STROKE DIESEL ENGINE USI...A REVIEW PAPER ON PERFORMANCE AND EMISSION TEST OF 4 STROKE DIESEL ENGINE USI...
A REVIEW PAPER ON PERFORMANCE AND EMISSION TEST OF 4 STROKE DIESEL ENGINE USI...ijsrd.com
 
Study and Review on Various Current Comparators
Study and Review on Various Current ComparatorsStudy and Review on Various Current Comparators
Study and Review on Various Current Comparatorsijsrd.com
 
Reducing Silicon Real Estate and Switching Activity Using Low Power Test Patt...
Reducing Silicon Real Estate and Switching Activity Using Low Power Test Patt...Reducing Silicon Real Estate and Switching Activity Using Low Power Test Patt...
Reducing Silicon Real Estate and Switching Activity Using Low Power Test Patt...ijsrd.com
 
Defending Reactive Jammers in WSN using a Trigger Identification Service.
Defending Reactive Jammers in WSN using a Trigger Identification Service.Defending Reactive Jammers in WSN using a Trigger Identification Service.
Defending Reactive Jammers in WSN using a Trigger Identification Service.ijsrd.com
 

More from ijsrd.com (20)

IoT Enabled Smart Grid
IoT Enabled Smart GridIoT Enabled Smart Grid
IoT Enabled Smart Grid
 
A Survey Report on : Security & Challenges in Internet of Things
A Survey Report on : Security & Challenges in Internet of ThingsA Survey Report on : Security & Challenges in Internet of Things
A Survey Report on : Security & Challenges in Internet of Things
 
IoT for Everyday Life
IoT for Everyday LifeIoT for Everyday Life
IoT for Everyday Life
 
Study on Issues in Managing and Protecting Data of IOT
Study on Issues in Managing and Protecting Data of IOTStudy on Issues in Managing and Protecting Data of IOT
Study on Issues in Managing and Protecting Data of IOT
 
Interactive Technologies for Improving Quality of Education to Build Collabor...
Interactive Technologies for Improving Quality of Education to Build Collabor...Interactive Technologies for Improving Quality of Education to Build Collabor...
Interactive Technologies for Improving Quality of Education to Build Collabor...
 
Internet of Things - Paradigm Shift of Future Internet Application for Specia...
Internet of Things - Paradigm Shift of Future Internet Application for Specia...Internet of Things - Paradigm Shift of Future Internet Application for Specia...
Internet of Things - Paradigm Shift of Future Internet Application for Specia...
 
A Study of the Adverse Effects of IoT on Student's Life
A Study of the Adverse Effects of IoT on Student's LifeA Study of the Adverse Effects of IoT on Student's Life
A Study of the Adverse Effects of IoT on Student's Life
 
Pedagogy for Effective use of ICT in English Language Learning
Pedagogy for Effective use of ICT in English Language LearningPedagogy for Effective use of ICT in English Language Learning
Pedagogy for Effective use of ICT in English Language Learning
 
Virtual Eye - Smart Traffic Navigation System
Virtual Eye - Smart Traffic Navigation SystemVirtual Eye - Smart Traffic Navigation System
Virtual Eye - Smart Traffic Navigation System
 
Ontological Model of Educational Programs in Computer Science (Bachelor and M...
Ontological Model of Educational Programs in Computer Science (Bachelor and M...Ontological Model of Educational Programs in Computer Science (Bachelor and M...
Ontological Model of Educational Programs in Computer Science (Bachelor and M...
 
Understanding IoT Management for Smart Refrigerator
Understanding IoT Management for Smart RefrigeratorUnderstanding IoT Management for Smart Refrigerator
Understanding IoT Management for Smart Refrigerator
 
DESIGN AND ANALYSIS OF DOUBLE WISHBONE SUSPENSION SYSTEM USING FINITE ELEMENT...
DESIGN AND ANALYSIS OF DOUBLE WISHBONE SUSPENSION SYSTEM USING FINITE ELEMENT...DESIGN AND ANALYSIS OF DOUBLE WISHBONE SUSPENSION SYSTEM USING FINITE ELEMENT...
DESIGN AND ANALYSIS OF DOUBLE WISHBONE SUSPENSION SYSTEM USING FINITE ELEMENT...
 
A Review: Microwave Energy for materials processing
A Review: Microwave Energy for materials processingA Review: Microwave Energy for materials processing
A Review: Microwave Energy for materials processing
 
Web Usage Mining: A Survey on User's Navigation Pattern from Web Logs
Web Usage Mining: A Survey on User's Navigation Pattern from Web LogsWeb Usage Mining: A Survey on User's Navigation Pattern from Web Logs
Web Usage Mining: A Survey on User's Navigation Pattern from Web Logs
 
APPLICATION OF STATCOM to IMPROVED DYNAMIC PERFORMANCE OF POWER SYSTEM
APPLICATION OF STATCOM to IMPROVED DYNAMIC PERFORMANCE OF POWER SYSTEMAPPLICATION OF STATCOM to IMPROVED DYNAMIC PERFORMANCE OF POWER SYSTEM
APPLICATION OF STATCOM to IMPROVED DYNAMIC PERFORMANCE OF POWER SYSTEM
 
Making model of dual axis solar tracking with Maximum Power Point Tracking
Making model of dual axis solar tracking with Maximum Power Point TrackingMaking model of dual axis solar tracking with Maximum Power Point Tracking
Making model of dual axis solar tracking with Maximum Power Point Tracking
 
A REVIEW PAPER ON PERFORMANCE AND EMISSION TEST OF 4 STROKE DIESEL ENGINE USI...
A REVIEW PAPER ON PERFORMANCE AND EMISSION TEST OF 4 STROKE DIESEL ENGINE USI...A REVIEW PAPER ON PERFORMANCE AND EMISSION TEST OF 4 STROKE DIESEL ENGINE USI...
A REVIEW PAPER ON PERFORMANCE AND EMISSION TEST OF 4 STROKE DIESEL ENGINE USI...
 
Study and Review on Various Current Comparators
Study and Review on Various Current ComparatorsStudy and Review on Various Current Comparators
Study and Review on Various Current Comparators
 
Reducing Silicon Real Estate and Switching Activity Using Low Power Test Patt...
Reducing Silicon Real Estate and Switching Activity Using Low Power Test Patt...Reducing Silicon Real Estate and Switching Activity Using Low Power Test Patt...
Reducing Silicon Real Estate and Switching Activity Using Low Power Test Patt...
 
Defending Reactive Jammers in WSN using a Trigger Identification Service.
Defending Reactive Jammers in WSN using a Trigger Identification Service.Defending Reactive Jammers in WSN using a Trigger Identification Service.
Defending Reactive Jammers in WSN using a Trigger Identification Service.
 

Recently uploaded

How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17Celine George
 
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRADUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRATanmoy Mishra
 
Maximizing Impact_ Nonprofit Website Planning, Budgeting, and Design.pdf
Maximizing Impact_ Nonprofit Website Planning, Budgeting, and Design.pdfMaximizing Impact_ Nonprofit Website Planning, Budgeting, and Design.pdf
Maximizing Impact_ Nonprofit Website Planning, Budgeting, and Design.pdfTechSoup
 
Benefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive EducationBenefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive EducationMJDuyan
 
Prescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxPrescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxraviapr7
 
M-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptxM-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptxDr. Santhosh Kumar. N
 
Diploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdfDiploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdfMohonDas
 
How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17Celine George
 
How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17Celine George
 
5 charts on South Africa as a source country for international student recrui...
5 charts on South Africa as a source country for international student recrui...5 charts on South Africa as a source country for international student recrui...
5 charts on South Africa as a source country for international student recrui...CaraSkikne1
 
The Stolen Bacillus by Herbert George Wells
The Stolen Bacillus by Herbert George WellsThe Stolen Bacillus by Herbert George Wells
The Stolen Bacillus by Herbert George WellsEugene Lysak
 
UKCGE Parental Leave Discussion March 2024
UKCGE Parental Leave Discussion March 2024UKCGE Parental Leave Discussion March 2024
UKCGE Parental Leave Discussion March 2024UKCGE
 
AUDIENCE THEORY -- FANDOM -- JENKINS.pptx
AUDIENCE THEORY -- FANDOM -- JENKINS.pptxAUDIENCE THEORY -- FANDOM -- JENKINS.pptx
AUDIENCE THEORY -- FANDOM -- JENKINS.pptxiammrhaywood
 
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfP4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfYu Kanazawa / Osaka University
 
How to Manage Cross-Selling in Odoo 17 Sales
How to Manage Cross-Selling in Odoo 17 SalesHow to Manage Cross-Selling in Odoo 17 Sales
How to Manage Cross-Selling in Odoo 17 SalesCeline George
 
Ultra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptxUltra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptxDr. Asif Anas
 
Practical Research 1 Lesson 9 Scope and delimitation.pptx
Practical Research 1 Lesson 9 Scope and delimitation.pptxPractical Research 1 Lesson 9 Scope and delimitation.pptx
Practical Research 1 Lesson 9 Scope and delimitation.pptxKatherine Villaluna
 
Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.EnglishCEIPdeSigeiro
 
Presentation on the Basics of Writing. Writing a Paragraph
Presentation on the Basics of Writing. Writing a ParagraphPresentation on the Basics of Writing. Writing a Paragraph
Presentation on the Basics of Writing. Writing a ParagraphNetziValdelomar1
 
Drug Information Services- DIC and Sources.
Drug Information Services- DIC and Sources.Drug Information Services- DIC and Sources.
Drug Information Services- DIC and Sources.raviapr7
 

Recently uploaded (20)

How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17
 
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRADUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
DUST OF SNOW_BY ROBERT FROST_EDITED BY_ TANMOY MISHRA
 
Maximizing Impact_ Nonprofit Website Planning, Budgeting, and Design.pdf
Maximizing Impact_ Nonprofit Website Planning, Budgeting, and Design.pdfMaximizing Impact_ Nonprofit Website Planning, Budgeting, and Design.pdf
Maximizing Impact_ Nonprofit Website Planning, Budgeting, and Design.pdf
 
Benefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive EducationBenefits & Challenges of Inclusive Education
Benefits & Challenges of Inclusive Education
 
Prescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxPrescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptx
 
M-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptxM-2- General Reactions of amino acids.pptx
M-2- General Reactions of amino acids.pptx
 
Diploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdfDiploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdf
 
How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17
 
How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17How to Use api.constrains ( ) in Odoo 17
How to Use api.constrains ( ) in Odoo 17
 
5 charts on South Africa as a source country for international student recrui...
5 charts on South Africa as a source country for international student recrui...5 charts on South Africa as a source country for international student recrui...
5 charts on South Africa as a source country for international student recrui...
 
The Stolen Bacillus by Herbert George Wells
The Stolen Bacillus by Herbert George WellsThe Stolen Bacillus by Herbert George Wells
The Stolen Bacillus by Herbert George Wells
 
UKCGE Parental Leave Discussion March 2024
UKCGE Parental Leave Discussion March 2024UKCGE Parental Leave Discussion March 2024
UKCGE Parental Leave Discussion March 2024
 
AUDIENCE THEORY -- FANDOM -- JENKINS.pptx
AUDIENCE THEORY -- FANDOM -- JENKINS.pptxAUDIENCE THEORY -- FANDOM -- JENKINS.pptx
AUDIENCE THEORY -- FANDOM -- JENKINS.pptx
 
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdfP4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
P4C x ELT = P4ELT: Its Theoretical Background (Kanazawa, 2024 March).pdf
 
How to Manage Cross-Selling in Odoo 17 Sales
How to Manage Cross-Selling in Odoo 17 SalesHow to Manage Cross-Selling in Odoo 17 Sales
How to Manage Cross-Selling in Odoo 17 Sales
 
Ultra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptxUltra structure and life cycle of Plasmodium.pptx
Ultra structure and life cycle of Plasmodium.pptx
 
Practical Research 1 Lesson 9 Scope and delimitation.pptx
Practical Research 1 Lesson 9 Scope and delimitation.pptxPractical Research 1 Lesson 9 Scope and delimitation.pptx
Practical Research 1 Lesson 9 Scope and delimitation.pptx
 
Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.Easter in the USA presentation by Chloe.
Easter in the USA presentation by Chloe.
 
Presentation on the Basics of Writing. Writing a Paragraph
Presentation on the Basics of Writing. Writing a ParagraphPresentation on the Basics of Writing. Writing a Paragraph
Presentation on the Basics of Writing. Writing a Paragraph
 
Drug Information Services- DIC and Sources.
Drug Information Services- DIC and Sources.Drug Information Services- DIC and Sources.
Drug Information Services- DIC and Sources.
 

An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks

  • 1. IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 08, 2014 | ISSN (online): 2321-0613 All rights reserved by www.ijsrd.com 207 An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks Naresh Kumar1 Arvind Sharma2 Gajendra Sajedia3 1 M. Tech Scholar 2,3 Assistance Professor 1,2,3 Department of Electronics and Communication Engineering 1,2,3 Rajasthan Institute of Engineering and Technology Abstract— Sensor networks are collection of sensor nodes which co-operatively send sensed data to base station. As sensor nodes are battery driven, an efficient utilization of power is essential in order to use networks for long duration. Therefore it is needed to reduce data traffic inside sensor networks, thereby reducing the amount of data that is needed to send to base station. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In wireless sensor network, periodic data sampling leads to enormous collection of raw facts, the transmission of which would rapidly deplete the sensor power. A fundamental challenge in the design of wireless sensor networks (WSNs) is to maximize their lifetimes. Data aggregation has emerged as a basic approach in WSNs in order to reduce the number of transmissions of sensor nodes, and hence minimizing the overall power consumption in the network. Data aggregation is affected by several factors, such as the placement of aggregation points, the aggregation function, and the density of sensors in the network. In this paper, an analytical model of wireless sensor network is developed and performance is analyzed for varying degree of aggregation and latency parameters. The overall performance of our proposed methods is evaluated using MATLAB simulator in terms of aggregation cycles, average packet drops, transmission cost and network lifetime. Finally, simulation results establish the validity and efficiency of the approach. Key words: Wireless sensor network, Data aggregation, Latency, Clustering I. INTRODUCTION Sensor networks are increasingly deployed for applications such as wildlife habitat monitoring, forest fire prevention, and military surveillance [1]. In these applications, the data collected by sensor nodes from their physical environment need to be assembled at a host computer or data sink for further analysis. Typically, an aggregate (or summarized) value is computed at the data sink by applying the corresponding aggregate function, e.g., MAX, COUNT, AVERAGE or MEDIAN to the collected data [2]. In large sensor networks, computing aggregates in-network, i.e., combining partial results at intermediate nodes during message routing, significantly reduces the amount of communication and hence the energy consumed. An approach used by several data acquisition systems for sensor networks is to construct a spanning tree rooted at the data sink, and then perform in-network aggregation along the tree. Partial results propagate level-by-level up the tree [3], with each node awaiting messages from all its children before sending a new partial result to its parent. Researchers have designed several energy-efficient algorithms for computing aggregates using the tree-based approach. Tree- based aggregation approaches, however, are not robust to communication losses which result from node and transmission failures and are relatively common in sensor networks. Because each communication failure loses an entire sub-tree of readings, a large fraction of sensor readings are potentially unaccounted for at the data sink, leading to a significant error in the aggregate computed. To address this problem, researchers have proposed novel algorithms that work in conjunction with multi-path routing for computing aggregates in lossy networks. Nevertheless, data aggregation provides a way to increase network lifetime of nodes by reducing the number of transmitted data packets, possibly at the cost of latency. With the advent of high functionality semiconductor IC's, any WSN node can be programmed to implement aggregation function at relatively no additional cost. This leads to considerable increase in network lifetime. Moreover, protocols such as LEACH (Low Energy Adaptive Clustering Hierarchy) ensure evenly dissipation of energy in cluster nodes thereby ensuring mechanisms to increase network life at higher layers. A. Data Aggregation and Data Security Data acquisition systems for sensor networks can be classified into two broad categories on the basis of data collection methodology employed for the application:  Query-based systems [4]  Event Based Systems [5] In query-based systems, the base station (the data sink) broadcasts a query to the network and the nodes respond with the relevant information. Messages from individual nodes are potentially aggregated to reroute to the base station. Finally, the base station computes one or more aggregate values based on the messages it has received. In some applications, queries may be persistent in nature resulting in a continuous stream of data being relayed to the data sink from the nodes in the network. For such applications, the query broadcast by the base station specifies a period (referred to as an epoch); nodes in the network send their readings to the base station after each epoch. In event-based applications, such as perimeter surveillance and biological hazard detection, nodes send a message to the base station only when the target event occurs in the area of interest. If multiple reports being relayed correspond to the same event, they can be combined by an intermediate node on the route to the base station. Data acquisition systems can also be categorized based on how sensor data is aggregated. In single-aggregator approaches, aggregation is performed only at the data sink. In contrast, hierarchical aggregation approaches make use of in-network aggregation. Hierarchical aggregation schemes can be further classified into tree-based schemes and ring- based schemes on the basis of the topology into which nodes are organized. Most existing data management and
  • 2. An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks (IJSRD/Vol. 2/Issue 08/2014/049) All rights reserved by www.ijsrd.com 208 acquisition systems for sensor networks are vulnerable to security attacks launched by malicious parties [6]. Sensor nodes are often deployed in unattended environments, so they are vulnerable to physical tampering. Since current sensor nodes lack hardware support for tamper-resistance, it is relatively easy for an adversary to compromise a node without being detected. The adversary can obtain confidential information (e.g., cryptographic keys) from the compromised sensor and reprogram it with malicious code. Moreover, the attacker can replicate the compromised node and deploy the replicas at various strategic locations in the network. A compromised node can be used to launch a variety of security attacks. These attacks include jamming at the physical or link layer as well as other resource consumption attacks at higher layers of the network software. Compromised nodes can also be used to disrupt routing protocols and topology maintenance protocols that are critical to the operation of the network. By using a few compromised nodes to render suspect the data collected at the sink, an adversary can effectively compromise the integrity and trustworthiness of the entire sensor network. In event-based systems, compromised nodes can be used to send false event reports to the base station with goal of raising false alarms and depleting the energy resources of the nodes in the network. This attack is referred to as the false data injection attack. Similarly, in query-based systems, compromised nodes can be used to inject false data into the network with the goal of introducing a large error in the aggregate value computed at the data sink. The aggregate computed by the sink is erroneous in the sense that it differs from the true value that would have been computed if there were no false data values included in the computation. Unlike event-based systems, however, in aggregation systems the effectiveness of a false data injection attack depends on both the aggregate being computed, e.g., MAX or MEDIAN, and whether sensor data is aggregated to re-route to the data sink or only at the data sink, and thus the techniques used for preventing this attack differ from the techniques used in event-based systems. In an aggregation system, a compromised node M can corrupt the aggregate value computed at the sink in four ways. First, M can simply drop aggregation messages that it is supposed to relay towards the sink. This has the effect of omitting a large fraction of sensor readings being aggregated. Second, M can alter a message that it is relaying to the data sink. Third, M can falsify its own sensor reading with the goal of influencing the aggregate value. Fourth, in systems that use in-network aggregation, M can falsify the sub-aggregate which it is supposed to compute based on the messages received from its child nodes. The first attack in which a compromised node intentionally drops aggregation messages can substantially deviate the final estimate of the aggregate if tree-based aggregation algorithms are used. The deviation will be large if the compromised node is located near the root of the aggregation hierarchy because a large fraction of sensor readings will be omitted from being aggregated. Countermeasures against this attack include the use of multi-path routing and ring-based topologies as well as the use of probabilistic techniques in the formation of aggregation hierarchies. To prevent the second attack in which a compromised node alters a message being relayed to the sink, it is necessary for each message to include a message authentication code (MAC) generated using a key shared exclusively between the originating node and the sink. This MAC enables the sink to check the integrity of a message, and filter out messages that have been altered. Hence, the effect of altering a message is no different from dropping it, and countermeasures such as multi-path routing are needed to mitigate the effect of this attack. The third attack in which a sensor intentionally falsifies its own reading is known as the falsified local value attack. This attack is similar to the behavior of nodes with faulty sensors, and also to the false data injection attack in event-based systems. Potential countermeasures to this attack include approaches used for fault tolerance such as majority voting and reputation-based frameworks. The three attacks discussed above apply to both single-aggregator and hierarchical aggregation systems, whereas the fourth attack applies only to hierarchical aggregation systems. This attack in which a node falsifies the aggregate value it is relaying to its parent(s) in the hierarchy is much more difficult to address. This attack is referred to as the falsified sub- aggregate attack. II. PROBLEM DEFINITION In wireless sensor network, data fusion is considered as an essential process for preserving sensor energy. Periodic data sampling leads to enormous collection of raw facts, the transmission of which would rapidly deplete the sensor power. Thus, aggregators are required to aggregate a collection of samples which can be further transmitted to the sink. This aggregator can be implemented in an existing wireless sensor node or can be of a distinct type other than the nodes. The requirement of aggregator is critical for increasing the network lifetime as the battery life of individual nodes can be depleted in sending large amount of raw data without any aggregation. However, data aggregation comes with the cost of latency in the network and thus, imposes a constraint when deploying the network in a setup with real time processing requirements. In this paper, a Cost Function is computed as the weighed sum of Energy conservation through aggregation and latency incurred sue to aggregation. The analytical model being developed is based on 250kbps, 2.4 GHz networks with AtMega128 microcontrollers [7] with TinyOS. This cost function enable to set up threshold values for data aggregation and latency tradeoffs so as to find suitable design of WSN for specific network lifetime and latency requirements of the deployment. A. Motivation Wireless sensor networks often consist of a large number of low-cost sensor nodes that have strictly limited sensing, computation, and communication capabilities. Due to resource restricted sensor nodes, it is important to minimize the amount of data transmission so that the average sensor lifetime and the overall bandwidth utilization are improved. Data aggregation is the process of summarizing and combining sensor data in order to reduce the amount of data transmission in the network. The cost of the aggregation is related with the cost of aggregator which is proportional to hardware/software capabilities. Thus, the cost of aggregator
  • 3. An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks (IJSRD/Vol. 2/Issue 08/2014/049) All rights reserved by www.ijsrd.com 209 is related with the complexity of the aggregation operation. However, data aggregation comes with the additional cost of latency. Data from various nodes, if needs to be aggregated at the aggregator, requires an additional time delay. This makes the use of such networks unsuitable for deployment for applications with soft and hard real time requirements. However, setting up threshold limits for both aggregation and latency, an optimal and economic design can be computed for deployment in particular set-up. In this paper, an analytical model has been developed to figure out the tradeoff between data aggregation and latency, and can be used to choose the type of deployment for particular physical conditions. B. Research Approach Mathematical model of WSN node based on ATmega microcontroller and TinyOS [8] operating system is being developed. Power dissipation is computed using first order radio energy dissipation model. Latency values are derived from the time complexity of the algorithms needed to aggregate the data at the cluster. It is assumed that LEACH is used for clustering and hence the dissipation of energy is almost identical for all the nodes for a complete duration of one round. Finally, a Cost Function is developed as the weighted sum of the energy savings due to clustering and data aggregation and the time delay (Latency) incurred due to aggregation is computed. The optimal value of the Cost function can be taken as reference for deployment of WSN in given physical conditions. Simulation of the model is done using MATLAB, and simulation results are presented along with the analytical results. This paper is organized as follows. Section 1 gives a brief overview of the paper. It discusses the scope and motivation for the subject matter of the paper in a comprehensive way. Section 2 provides the problem statement and motivation.. Section 3 presents the analytical model for the Energy dissipation using First order Radio Electronics. It further derives equation for computation of energy savings due to data aggregation and latency incurred due to aggregation algorithms. Section 4 discusses the results derived through analytical models and simulation code. Section 5 discusses the conclusion and future scope of the work. III. PROPOSED WORK A. Power Consumption Prospective: Data Aggregation and Data Transmission Energy consumption problem, being the most visible challenge, is considered central to the sensor research theme. The processing of data, memory accesses and input/output operations, all consume sensor energy. However, the major power drain occurs due to wireless communication. Therefore, attempts require to be carried out to perform as much in-network processing as possible within a sensor or a group of sensors (cluster). This is achieved by performing aggregation and filtration of raw data before transmitting them to destined targets. As a result of which redundancy in the recorded sensory samples is eliminated, thereby reducing the transmission cost and network overloading. Moreover, decrease in the effective number of packet transmissions also leads to minimized chances of network congestion, thereby saving the excess energy consumption in the network, possibly at the expense of latency in the operation. Moreover, computation at intermediate node, possibly for calculation of average, mean etc., requires additional logic circuitry leading to increased cost and complexity. The above facts can be explained using a simple example with realistic values of quantities. If the radio electronics requires 50nJ/bit and amplifier circuitry needs 10pJ/bit/m2 for communication, then power used in transmitting 1 bit of information to the processing centre situated 1 km away, consumes 1.005 × 104 nJ per unit time (watts). However, energy used in data processing for aggregation is 5nJ/bit/signal, which implies that execution of almost 2010 instructions compensates the energy used for one transmission in unit time. Therefore, it is quite recommendable to apply aggregation techniques. Previous researches have already proven the fact that in-network processing cost is much less than the communication cost. The proliferation of sensor network has created the urge of exploring novel ideas for data aggregation. However, the aggregation schemes would require efficient clustering protocols to well-implement its functioning. Therefore, efficient data aggregation techniques are to be used in conjunction with efficient clustering protocols so as to give optimum performance under given network constraints. B. Radio Energy Dissipation Model Radio energy consumption for TX and RX is measured based on TinyOS 2.x. The computation of energy consumption for compression and decompression is measured based on the running time of each algorithm targeted at ATmega128L, which is a micro-controller unit (MCU) used in many mote- class sensor nodes. Total energy consumptions comprising radio energy and CPU energy before and after applying compression with respect to data size can be computed using First order radio energy consumption model. Processing delays, which eventually affect the end-to-end latency can also be estimated. Energy consumption before and after compression in a simple topology consisting of sender and receiver can be measured in a simple way. In a normal packet transmission scenario, i.e., before-compression, radio energy is consumed for transmitting the uncompressed data. On the other hand, if we apply data compression, i.e., after- compression, a sender transmits a packet with smaller data length, reducing the radio energy. Since the packet length in TinyOS is limited to 114 bytes, packets having larger than such a limitation are split into multiple fragments. Computational energy required for running the data compression algorithm can be measured using standard equations. Fig. 3.1 A typical Transmitter and Receiver System
  • 4. An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks (IJSRD/Vol. 2/Issue 08/2014/049) All rights reserved by www.ijsrd.com 210 1) Uncompressed Data transmitted from all nodes to Sink Consider the following schematic diagram for illustration of radio energy dissipation: Fig 3.2 WSN without clustering and Data Aggregation (Every node senses the data and transmits to sink) The first order radio model is considered in with ATMEGA parameter values are considered. The energy spent in transmission of single bit to d distance is given by: ( ) where et1 is the energy dissipated per bit in the transmitter circuitry and ed1dn is the energy dissipated for transmission of a single bit over a distance d, n being the path loss exponent (usually 2.0≤n≤4.0). For simulation purposes we have considered a first order model where we assume n=2. Thus the total energy dissipated for transmitting a K-bit packet is ( ) ( ) ( ) where et=et1*K and ed= ed1*K. If er1 be the energy required per bit for successful reception then energy dissipated for receiving a K-bit packet is ( ) where, er=er1*K. In simulations model considered, et1= 50 nJ/bit, ed1= 100 pJ/bit/m2 and er1= et1 with K = 2000 bits. It is assumed that the channel is symmetric so that the energy spent in transmitting from node i to j is the same as that of transmitting from node j to i for any Signal to Noise Ratio. Let the mean number of packets transmitted to the sink by each node assuming that all the nodes are equidistant from the sink, is λ. Considering Poisson Probability Distribution for packets transmitted by each node to the sink, the following equations can be derived. ( ) ( ) where λ is the mean number of data packets transmitted by each node in the network in a unit time, R is the number of bits per packet and N is the mean number of transmitting nodes in the network. Also, ( ) Thus, the total energy consumption in the network is: ( ) ( ) It can be shown that total energy consumption for TX and for the RX is less in case of compressed data as compared with uncompressed data transmission. For a given dataset, it can be shown that the total energy consumption for transmission and reception with after-compression is less than that of the normal packet transmission and reception, i.e., before-compression. The amount of reduced radio energy for transmitting and receiving packets with the decreased data length due to compression is larger than the amount of increased computation energy for compression in TX and for decompression in RX. For the two terms as stated in the above equations on the RHS, the latter term denotes the amount of energy dissipated in reception process. If all nodes are programmed to transmit to the sink in the absence of any clustering, then no intermediate node can receive the packet. Also, the sink at which all the data packets arrives is plentiful and abundant in all resources needed. Thus, without any loss of generality in the specified configuration, the energy that corresponds to data reception at the sink can be ignored as that cannot affect network lifetime to any measurable extent. 2) Cluster Formation and Data Aggregation. If the nodes in the network are suitably partitioned into clusters of suitable size, then each cluster approximately would have N/C nodes, where N is the mean number of transmitting nodes in the network and C is the number of clusters. Consider the data aggregation at a cluster head. Fig 3.3 Data Aggregation using Cluster Heads (Red Nodes depicts Cluster Heads) Consider the case of a single cluster. The energy dissipation for data aggregation can be computed in a simple way. ATmega128L used in this analysis is a 8 bit microcontroller meaning that it processes 8 bits per cycle. Assuming that is dissipates J joules per cycle, the cycles needed to process bits is . However, the number of CPU cycles needed to perform the analysis is much more than this and depend upon the type of the compression operation required in view of particular deployment of the sensor network. Let S denotes the number of CPU cycles needed to aggregate the data in the desired way. Thus, S gives a measure of the complexity of the process of data aggregation performed by the cluster head. Then ( ) S gives the estimated number of cycles needed to perform the aggregation by the Cluster Head (CH). T must be chosen suitably so as to keep an upper limit on the number of cycles needed to perform the computation, provided a given data compression/aggregation algorithm is provided. The energy consumption in a cluster can be computed in the following way. The energy consumed by the cluster head in data aggregation is: ( )
  • 5. An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks (IJSRD/Vol. 2/Issue 08/2014/049) All rights reserved by www.ijsrd.com 211 Here, J refers to the energy consumption per CPU cycle. The mean number of packets arrived at the cluster head per unit time is . However, aggregation compresses the data and produce resultant data in a compressed form. Assuming α be the compression factor, the number of bits produced after compression is The energy consumed in transmission by all nodes of a cluster, to the CH, in some specific cluster is ( ) ( ) Here d' denotes the distance of cluster head from the nodes in the cluster. One simple assumption without the loss of generality can be taken as d' = d/C where C is the number of cluster heads. Also, energy consumption by the CH in transferring a K bit aggregated packet to the sink is ( ) ( ) The term d' is considered again for the argument stated above. The energy consumption at the sink is ( ) ( ) This term can be ignored as the energy consumption at the sink is negligible as compared to the resources available with it. Thus, the total energy consumption per cluster in the said approach is Energy consumption in complete network can be obtained by multiplying with the number of clusters in the network. Thus, the total energy consumption in the network is where C is the number of clusters. Section 4 shows the results of the energy saving for data operations for varying values of T. 3) Cluster Formation without Data Aggregation Without data aggregation, the complete data in its entirety, as received by the cluster heads from the individual nodes in the cluster is to be transmitted to the sink. Therefore, in this scenario, where ( ) ( ) is defined with value of K having α =1, and . Section 4 gives a relative comparison between each of the three schemes of data transfer. 4) Data Aggregation and Latency Latency gives a measure of delay incurred due to intermediate processing at the cluster heads. Without data aggregation, latency can be measured as the time complexity of data aggregation at the sink, or simply, ( ) Where N is the number of nodes in the network and O(N) refers to the Big O notation for an upper bound on the given function of data aggregation. Assuming a beacon oriented MAC layer implementation, in a clustered network, the CH receives the data packets from all the nodes almost simultaneously. The time complexity of the aggregation operation is a function of N/C. Thus ( ) However, the data from all the CH is again aggregated at the sink, leading to a latency factor at the sink itself. ( ) Data can be transferred in a clustered network directly to the sink, without data aggregation. In this case, the overall latency of network can be computed as the latency in the uncluttered network, plus an additional parameter to account for the TDMA schedule generated by the cluster head and its corresponding transmission of the data to the sink. Thus, for this configuration, ( ) where β is a constant to account for the proportionate delay. ( ) Section 4 gives the analysis of the equation based on the model of First order radio electronics power dissipation and latency. C. Optimal Number of Cluster for Given latency and Energy Constraints Clustering is the process of assigning a set of sensor nodes, with similar attributes, to a specified group or cluster. More number of nodes in any cluster signifies more processing at CH, thereby increased battery life of the nodes in the cluster but dissipation of more energy at CH in view of processing of large volume of data. Thus, energy saving in data transmission for nodes in the cluster will have an adverse effect on the power consumption due to intense processing at cluster head. Also, it leads to an increased latency as the cluster head is a simple cluster and cannot match the processing capabilities of full functional processing machines like sink. Less nodes per cluster means more clusters and increased latency at sink due to data from multiple cluster heads. However, it reduces latency at CH as low data in view of small nodes per cluster needs to be processed. However, an overall gain in terms of latency is obtained as CH is one of the node elected as a leader through some leader election algorithm (e.g. LEACH) whereas the sink usually have much more computing power as compared to the sensor node. Moreover, the energy consumption at the CH is less in view of less CPU cycles needed to aggregate the data. It is assumed that formation of cluster follows a procedure that includes the type of sensed data, geographical positioning and distance between the nodes. Nevertheless, clustering is almost unavoidable for a large class of sensor networks. However, parametrically, certain analysis can be drawn taking C (number of clusters) as the key parameter. In the current analysis, a function is constructed as Cost Function, and it is to be analyzed for varying values of C and traffic conditions. The cost function is to be computed as to give the overall performance value of the network under specified parameters. Consider the following equation for Cost Function:
  • 6. An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks (IJSRD/Vol. 2/Issue 08/2014/049) All rights reserved by www.ijsrd.com 212 Here, u and v are suitable constants so as to match the values and to emphasize on any one of the feature as the case may be. Section 4 analyzes the value of Cost Function against standard values of model parameters and conclusions are drawn. IV. ANALYSIS OF PROPOSED WORK A. Data Aggregation and Latency For simulation purpose, the parameters used correspond to TinyOS on ATmega128L microcontroller. Consider the following maximum values of parameter: Parameter Symbol and Description. Value Data Rate, Frequency Band 250kbps, 2.4 GHz Transmission Range 10m/20m N; Number of Nodes in the Network, considering all as the active nodes 1000 C; Number of Clusters in the Network [0, 0.05]; Vector ranging from 0 to 0.05 percent of the number of nodes in the cluster. λ ; Mean number of Packets transmitted by each node per unit time [0,280], corresponding to 250kbps data rate abd 114 byte packet size. R; Bits per Packet 114*8 (TinyOS Specification) ; energy dissipation per bit in transmitter circuitry before transmission 50nJ/bit ; energy dissipation per bit in sending a bit over d distance. 10pJ/bit/m2 ; energy dissipation per bit in receiver circuitry. 50nJ/bit J; Energy Consumption of ATmega128L per CPU cycle. Being 8 bit microcontroller, it processes 8 bits per unit time. 100nJ/bit d; mean distance of nodes from the sink. [1,20] d' ; mean distance of cluster heads from the sink [1,20]/α; where α depends upon the particular deployment of the sensor network. Table 4.1parameter Specification Of Values Of Variables In The Analytical Analysis The following results are obtained corresponding to the set of values as specified in table 4.1 Fig. 4.1 Power Consumption of a typical Transmitter Receiver Pair as a function of number of packets transferred and distance between the nodes. Fig. 4.2 Power Consumption as a function of number of packets transmitted and number of nodes (assuming distance between the nodes is 10 mtrs). Fig. 4.3 Power Consumption as a function of number of packets transmitted and mean distance between nodes and sink. Fig. 4.4 Power Consumption as a function of number of nodes and mean distance between nodes and sink. Fig. 4.5 Power Consumption as a function of distance of nodes from cluster heads and number of nodes.
  • 7. An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks (IJSRD/Vol. 2/Issue 08/2014/049) All rights reserved by www.ijsrd.com 213 Fig. 4.6 Latency (Time Delay) in WSN with clustering but without data aggregation . Fig. 4.7 Power consumption in WSN as a function of data aggregation (compression) and number of nodes. Fig. 4.8 Latency of WSN as a function of Aggregation factor and percentage of CHs B. Calculation of Optimal Number of Clusters for Given latency and Energy Constraints Consider T be the upper bound on latency and E be the upper bound on energy consumption. The cost function to be minimized, as specified in section 3 is Where and . Analytical results can be derived to find the optimal number of cluster head percentage for any Wireless Sensor Network. Table 4.3 can be derived from the parameter values as specified in table 4.1 above: Other parameter values considered are: Parameter Value α ; Compression Factor for Data; (1/ α) Delay Factor in Latency Computation 0.1 AA Battery Life AA: (2.85 Ah) x (1.5 V) x (3600 s) = 15,390 J Number of Nodes 1000 Packet Transmission Rate 280 pkts per second. Table 4.2: Parameter Considered In Table 4.3 CH proporti on C H N C H power consumptio n Life Laten cy CF 0 0 10 00 25.57277 0 998.0 146 99.80 146 0.002 2 99 8 25.52183 29.12 261 998.0 073 128.9 233 0.004 4 99 6 25.47089 29.18 108 498.0 036 78.98 145 0.006 6 99 4 25.41994 29.23 98 331.3 357 62.37 337 0.008 8 99 2 25.369 29.29 875 248.0 018 54.09 893 0.01 1 0 99 0 25.31806 29.35 794 198.0 014 49.15 808 0.012 1 2 98 8 25.26711 29.41 737 164.6 679 45.88 416 0.014 1 4 98 6 25.21616 29.47 704 140.8 582 43.56 286 0.016 1 6 98 4 25.16522 29.53 695 123.0 009 41.83 704 0.018 1 8 98 2 25.11427 29.59 711 109.1 119 40.50 83 0.02 2 0 98 0 25.06332 29.65 751 98.00 071 39.45 758 0.022 2 2 97 8 25.01237 29.71 816 88.90 974 38.60 913 0.024 2 4 97 6 24.96142 29.77 906 81.33 393 37.91 245 0.026 2 6 97 4 24.91047 29.84 021 74.92 362 37.33 257 0.028 2 8 97 2 24.85951 29.90 161 69.42 908 36.84 451 0.03 3 0 97 0 24.80856 29.96 326 64.66 714 36.42 997 0.032 3 2 96 8 24.75761 30.02 517 60.50 044 36.07 521 0.034 3 4 96 6 24.70665 30.08 733 56.82 394 35.76 972 0.036 3 6 96 4 24.6557 30.14 975 53.55 595 35.50 535 0.038 3 8 96 2 24.60474 30.21 243 50.63 195 35.27 563 0.04 4 0 96 0 24.55378 30.27 538 48.00 035 35.07 541 0.042 4 2 95 8 24.50283 30.33 858 45.61 938 34.90 052 0.044 4 4 95 6 24.45187 30.40 205 43.45 486 34.74 754
  • 8. An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks (IJSRD/Vol. 2/Issue 08/2014/049) All rights reserved by www.ijsrd.com 214 0.046 4 6 95 4 24.40091 30.46 579 41.47 856 34.61 364 0.048 4 8 95 2 24.34995 30.52 979 39.66 696 34.49 649 0.05 5 0 95 0 24.29899 30.59 406 38.00 028 34.39 409 0.052 5 2 94 8 24.24802 30.65 861 36.46 18 34.30 479 0.054 5 4 94 6 24.19706 30.72 343 35.03 729 34.22 715 0.056 5 6 94 4 24.1461 30.78 852 33.71 453 34.15 997 0.058 5 8 94 2 24.09513 30.85 389 32.48 3 34.10 219 0.06 6 0 94 0 24.04417 30.91 953 31.33 356 34.05 289 0.062 6 2 93 8 23.9932 30.98 546 30.25 829 34.01 129 0.064 6 4 93 6 23.94223 31.05 167 29.25 021 33.97 669 0.066 6 6 93 4 23.89127 31.11 816 28.30 324 33.94 848 0.068 6 8 93 2 23.8403 31.18 494 27.41 196 33.92 613 0.07 7 0 93 0 23.78933 31.25 2 26.57 162 33.90 916 0.072 7 2 92 8 23.73836 31.31 935 25.77 797 33.89 715 0.074 7 4 92 6 23.68739 31.38 7 25.02 721 33.88 972 0.076 7 6 92 4 23.63641 31.45 494 24.31 597 33.88 653 0.078 7 8 92 2 23.58544 31.52 317 23.64 12 33.88 729 0.08 8 0 92 0 23.53447 31.59 17 23.00 017 33.89 171 0.082 8 2 91 8 23.48349 31.66 052 22.39 041 33.89 956 0.084 8 4 91 6 23.43252 31.72 965 21.80 968 33.91 062 0.086 8 6 91 4 23.38154 31.79 908 21.25 597 33.92 468 0.088 8 8 91 2 23.33057 31.86 882 20.72 742 33.94 156 0.09 9 0 91 0 23.27959 31.93 886 20.22 237 33.96 109 0.092 9 2 90 8 23.22861 32.00 921 19.73 927 33.98 313 0.094 9 4 90 6 23.17763 32.07 987 19.27 674 34.00 754 0.096 9 6 90 4 23.12665 32.15 084 18.83 347 34.03 419 0.098 9 8 90 2 23.07567 32.22 213 18.40 83 34.06 296 0.1 1 0 0 90 0 23.02469 32.29 373 18.00 013 34.09 375 Table 4.3: Computation of Cost Function Figure 4.9 Latency (time delay) plotted against the proportion of cluster heads. Horizontal axis shows the proportion of Cluster Heads while vertical axis shows the latency. Figure 4.10 Network life plotted against the proportion of cluster heads. Horizontal axis shows the proportion of Cluster Heads while vertical axis shows the network lifetime. Figure 4.11 Overall power consumption in the WSN. Horizontal axis shows the proportion of Cluster Heads while vertical axis shows the power consumption in joules Figure 4.12 Cost Function plotted against cluster heads. Horizontal axis shows the proportion of Cluster Heads while vertical axis shows the value of cost function as the sum of network lifetime and Latency. Section 5 discusses the result of the paper and outline the conclusion and future scope. 0 1000 2000 0 0.014 0.028 0.042 0.056 0.07 0.084 0.098 Latency Latency 26 28 30 32 34 0 0.014 0.028 0.042 0.056 0.07 0.084 0.098 Network Life Network Life 10 11 12 13 0 0.016 0.032 0.048 0.064 0.08 0.096 Power Consumption power consumpt ion 33 33.5 34 34.5 35 35.5 0.038 0.048 0.058 0.068 0.078 0.088 0.098 Cost Function Cost Function
  • 9. An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks (IJSRD/Vol. 2/Issue 08/2014/049) All rights reserved by www.ijsrd.com 215 V. CONCLUSION AND FUTURE SCOPE This paper provides a detailed review of secure data aggregation concept in wireless sensor networks Data Aggregation is a technique for reducing the communication overhead and optimizing the bandwidth utilization in the wireless links. To give the motivation behind secure data aggregation, first, the energy dissipation computation of wireless sensor networks are presented without clustering and aggregation and the relationships between data aggregation concept with energy consumption and latency are explained. Second, an extensive literature survey is presented by summarizing the state-of-the-art data aggregation and clustering protocols. Based on this extensive literature survey, open research areas and future research directions are given. However, data aggregation techniques raises privacy and security issues of the sensor nodes which need to share their data with the aggregator node. One of the most critical issue raised by aggregation is latency or time delay that occur due to the complexity of the aggregation function. In this paper, a mathematical model is developed for providing an analytical estimate to the increase in network life due to reduction in in-network communication. This is a direct consequence of data aggregation as it leads to decrease in number of data packets for any transmission between two nodes. This analysis is based on energy dissipation model based on first order radio energy dissipation. The same model is extended to include the delay that incurred due to data aggregation algorithms executed at the node. Moreover, the sensor node, acting as the aggregator needs to wait for data packets from the entire cluster before aggregation can actually be performed. This supplements the latency in the network as a result of clustering and data aggregation. Section 4 presents the results for the mathematical expression derived for energy consumption and latency tradeoff. It also derives the Cost Function with energy dissipation and latency as parameter values. The cost function is computed as the weighted sum of network lifetime and latency and can be used as a reference so as to setup a wireless sensor network under given physical constraints of network lifetime and latency. Computation of latency is becoming increasingly important in conjunction with the hard and soft real time requirements for almost all the classes of networks. Moreover, for human life rated networks such a fire or radiation sensors, latency is one of the most critical element. A. Future Scope As a future scope of this research, it is intended to consider a stochastic model of the wireless sensor network under prescribed network traffic condition to model the energy dissipation more accurately as compared to presented in current work. Also, a more accurate model of radio energy dissipation is to be considered to account for more accurate energy dissipation for a given class of hardware devices. Implementation of a general class of aggregation functions is to be computed for measurement of network life latency tradeoffs. REFERENCES [1] Zhiyong Peng; Xiaojuan Li, "The improvement and simulation of LEACH protocol for WSNs," Software Engineering and Service Sciences (ICSESS), 2010 IEEE International Conference on , vol., no., pp.500,503, 16-18 July 2010 doi: 10.1109/ICSESS.2010.5552317 [2] Yuhua Liu; Jingju Gao; Longquan Zhu; Yugang Zhang, "A Clustering Algorithm Based on Communication Facility in WSN," Communications and Mobile Computing, 2009. CMC '09. WRI International Conference on , vol.2, no., pp.76,80, 6-8 Jan. 2009. doi: 10.1109/CMC.2009.236 [3] He, C.; Kiziroglou, Michail E.; Yates, D.C.; Yeatman, E.M., "A MEMS Self-Powered Sensor and RF Transmission Platform for WSN Nodes," Sensors Journal, IEEE, vol.11, no.12, pp.3437,3445, Dec. 2011 doi: 10.1109/JSEN.2011.2160535. [4] Van-Trinh Hoang; Julien, N.; Berruet, P., "Design under constraints of availability and energy for sensor node in wireless sensor network," Design and Architectures for Signal and Image Processing (DASIP), 2012 Conference on , vol., no., pp.1,8, 23- 25 Oct. 2012 [5] Khan, S.; Eui-Nam Huh; Rao, I, "Reliable Data Dissemination with Diversity Multi-Hop Protocol for Wireless Sensors Network," Advanced Communication Technology, 2008. ICACT 2008. 10th International Conference on, vol.1, no., pp.9, 13, 17-20 Feb. 2008. doi: 10.1109/ICACT.2008.4493700 [6] Wen-Wen Huang; Ya-li Peng; Jian Wen; Min Yu, "Energy-Efficient Multi-hop Hierarchical Routing Protocol for Wireless Sensor Networks," Networks Security, Wireless Communications and Trusted Computing, 2009. NSWCTC '09. International Conference on , vol.2, no., pp.469,472, 25-26 April 2009 doi: 10.1109/NSWCTC.2009.352 [7] Lihua Xiao; Qiong Liu, "A data fusion using un-even clustering for WSN," Advanced Intelligence and Awareness Internet (AIAI 2011), 2011 International Conference on , vol., no., pp.216,219, 28-30 Oct. 2011. doi: 10.1049/cp.2011.1460 [8] Deosarkar, B.P.; Yadav, N.S.; Yadav, R. P., "Clusterhead selection in clustering algorithms for wireless sensor networks: A survey," Computing, Communication and Networking, 2008. ICCCn 2008. International Conference on , vol., no., pp.1,8, 18-20 Dec. 2008 doi: 10.1109/ICCCNET.2008.4787686 [9] Aldalahmeh, S.; Ghogho, M., "Traffic estimation for MAC protocols in distributed detection wireless sensor networks," Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European , vol., no., pp.719,723, 27-31 Aug. 2012 [10]Murillo, AF.; Pena, M.; Martinez, D., "Applications of WSN in health and agriculture," Communications Conference (COLCOM), 2012 IEEE Colombian , vol., no., pp.1,6, 16-18 May 2012 .doi: 10.1109/ColComCon.2012.6233678. [11]Xinsheng Xia; Qilian Liang, "Latency-aware and energy efficiency tradeoffs for wireless sensor networks," Personal, Indoor and Mobile Radio Communications, 2004. PIMRC 2004. 15th IEEE
  • 10. An Analytical Model of Latency and Data Aggregation Tradeoff in Cluster Based Wireless Sensor Networks (IJSRD/Vol. 2/Issue 08/2014/049) All rights reserved by www.ijsrd.com 216 International Symposium on , vol.3, no., pp.1782,1786 Vol.3, 5-8 Sept. 2004.doi: 10.1109/PIMRC.2004.1368306 [12]Sivaraj, R.; Thangarajan, R., "Location and Time Based Clone Detection in Wireless Sensor Networks," Communication Systems and Network Technologies (CSNT), 2014 Fourth International Conference on , vol., no., pp.133,137, 7-9 April 2014 doi: 10.1109/CSNT.2014. [13]El-Aaasser, M.; Ashour, M., "Energy aware classification for wireless sensor networks routing," Advanced Communication Technology (ICACT), 2013 15th International Conference on , vol., no., pp.66,71, 27-30 Jan. 2013. [14]Zhao Jun; Chen Xiang-guang; Xie Ying-xin, "The application of multi-path fault tolerant algorithm in WSN nodes," Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on , vol., no., pp.7323,7326, 8-10 Aug. 2011 doi: 10.1109/AIMSEC.2011.6010112. [15]Dhasian, H.R.; Balasubramanian, P., "Survey of data aggregation techniques using soft computing in wireless sensor networks," Information Security, IET , vol.7, no.4, pp.336,342, December 2013 doi: 10.1049/iet-ifs.2012.0292 [16]Horvat, Goran; Zagar, Drago; Vinko, Davor, "Influence of node deployment parameters on QoS in large-scale WSN," Embedded Computing (MECO), 2014 3rd Mediterranean Conference on , vol., no., pp.202,205, 15-19 June 2014. [17]Salman, N.; Rasool, I; Kemp, AH., "Overview of the IEEE 802.15.4 standards family for Low Rate Wireless Personal Area Networks," Wireless Communication Systems (ISWCS), 2010 7th International Symposium on , vol., no., pp.701,705, 19-22 Sept. 2010 doi: 10.1109/ISWCS.2010.5624516 [18]Ghosh, P.; Mayo, M.; Chaitankar, V.; Habib, T.; Perkins, E.; Das, S.K., "Principles of genomic robustness inspire fault-tolerant WSN topologies: A network science based case study," Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011 IEEE International Conference on , vol., no., pp.160,165, 21-25 March 2011. doi: 10.1109/PERCOMW.2011.5766861 [19]Serfass, D.; Yoshigoe, K., "Wireless sensor networks using Android Virtual Devices and Near Field Communication peer-to-peer emulation," Southeastcon, 2013 Proceedings of IEEE , vol., no., pp.1,6, 4-7 April 2013. doi: 10.1109/SECON.2013.6567465. [20]Chatterjee, A; Mukherjee, D., "Variety event detection in Wireless Sensor Networks through single hop cluster topology," Wireless and Optical Communications Networks (WOCN), 2013 Tenth International Conference on , vol., no., pp.1,5, 26-28 July 2013 doi: 10.1109/WOCN.2013.6616201 [21]Tianbo Wang; Wu, Chengdong; Peng Ji; Jian Zhang, "A noble data aggregation algorithm for low latency in wireless sensor network," Mechatronics and Automation (ICMA), 2010 International Conference on , vol., no., pp.894,898, 4-7 Aug. 2010 doi: 10.1109/ICMA.2010.5589006 [22]Chunyao Fui; Zhifang Jiangi; Wei Wei; Ang Wei, " An Energy Balanced Algorithm of LEACH Protocol in WSN ", IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 1, No 1, January 2013.