heterogeneous sensor networks
DESCRIPTION
this provides complete description about WSN.TRANSCRIPT
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CHAPTER 1
INTRODUCTION
Wireless sensor networks attracted lots of researchers because of its
potential wide applications and special challenges. Early study on wireless sensor
networks mainly focused on technologies based on the homogeneous wireless
sensor network in which all nodes have same system resource. However,
heterogeneous wireless sensor network is becoming more and more popular
recently. And the result show that heterogeneous nodes can prolong network
lifetime and improve network reliability without significantly increasing the cost.
A typical heterogeneous wireless sensor networks consists of a large number of
normal nodes and a few heterogeneous nodes. The normal node, whose main
tasks are to sense and issue data report, is inexpensive and source-constrained.
The heterogeneous node, which provides data filtering, fusion and
transport, is more expensive and more capable. It may possess one or more type
of heterogeneous resource, e.g., enhanced energy capacity or communication
capability. They may be line powered, or their batteries may be replaced easily.
Compared with the normal nodes, they may be configured with more powerful
microprocessor and more memory. They also may communicate with the sink
node via high-bandwidth, long-distance network, such as Ethernet. The presence
of heterogeneous nodes in a wireless sensor network can increase network
reliability and lifetime.
There are three common types of resource heterogeneity in sensor
node: computational heterogeneity, link heterogeneity, and energy heterogeneity.
Computational heterogeneity means that the heterogeneous node has a more
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powerful microprocessor and more memory than the normal node. With the
powerful computational resources, the heterogeneous nodes can provide complex
data processing and longer-term storage.
Link heterogeneity means that the heterogeneous node has high-
bandwidth and long-distance network transceiver, than the normal node. Link
heterogeneity can provide more reliable data transmission.
Energy heterogeneity means that the heterogeneous node is line
powered, or its battery is replaceable. Among above three types of resource
heterogeneity, the most important heterogeneity is the energy heterogeneity
because both computational heterogeneity and link heterogeneity will consume
more energy resource. If there is no energy heterogeneity, computational
heterogeneity and link heterogeneity will bring negative impact to the whole
sensor network, i.e., decreasing the network lifetime.
Figure 1.1 Node hardware architecture
1.1 THE IMPACT OF HETEROGENEOUS RESOURCES
Placing few heterogeneous nodes in the sensor network can bring
following three main benefits
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1.1.1 Prolonging Network Lifetime
In the heterogeneous wireless sensor network, all normal nodes can
send data report to the sink via the nearest heterogeneous node. And the normal
nodes, especially around the sink, don’t need forward vast packets from other
nodes. Under this data transmission scheme, the typical hops of data transmission
are 2 and significantly less than the average hops in homogeneous sensor
network. In other words, the average energy consumption for forwarding a
packet from the normal nodes to the sink in heterogeneous sensor networks will
be much less than the energy consumed in homogeneous sensor networks. With
the size of network increasing, the gap of energy consumption between these two
kinds of networks will be bigger and bigger.
1.1.2 Improving Reliability of Data Transmission
It is well know that sensor network links tend to have low reliability
and each hop significantly lowers the end-to-end delivery rate. In heterogeneous
nodes, there will be fewer hops between normal sensor nodes and the sink. So the
heterogeneous sensor network can get much higher end-to-end delivery rate than
the homogeneous sensor network.
1.1.3 Decreasing Latency of Data Transportation
Computational heterogeneity can decrease the processing latency in
immediate nodes and link heterogeneity can decrease the waiting time in the
transmitting queue. Fewer hops between sensor nodes and sink node also mean
fewer forwarding latency.
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1.2 CHALLENGES AND ISSUES IN CLUSTERING THE WSNs
Despite the tremendous potentials and its numerous advantages like
distributed localized computing in which failure of one part of the network does
not affect the operation in another part of the network, wide area coverage,
extreme environment area monitoring, WSNs pose various challenges to the
research community. This section briefly summarizes some of the major
challenges faced while clustering the WSNs.
1.2.1 Node Deployment
Node deployment in WSNs is either fixed or random depending on
the application. In fixed deployment the nodes are deployed on predetermined
locations whereas in random deployment the resulting distribution can be
uniform or non-uniform. In such a case careful management of the network is
necessary in order to ensure maximum area coverage and also to ensure uniform
energy consumption across the network.
1.2.2 Heterogeneous Network
Nodes in the WSNs are always not uniform in terms of architecture
functionality and life time. In these cases the network is heterogeneous. Some
nodes are less energy constrained than others. Usually the fraction of nodes
which are less energy constrained is small. In such a type of network the less
energy constraint nodes are chosen as cluster head of a cluster and the energy
constrained nodes are the worker nodes of the cluster. The problem arises in such
a network when the network is deployed randomly and all cluster heads are
concentrated in some particular part of the network resulting in Multilayer
Cluster Based Energy Efficient Routing Protocol unbalanced cluster formation
and also making some portion of the network unreachable. Also, if the resulting
distribution of the cluster heads is uniform and if we use multi-hop
communication, the nodes which are close to the cluster head are under heavy
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load as all the traffic is routed from different areas of the network to the cluster
head is via the neighbors of the cluster head. This will cause quick dying of the
nodes in the vicinity of the cluster heads resulting in holes near the cluster heads
and increasing the energy consumption. Heterogeneous sensor networks require
careful management of the clusters in order to avoid the problems resulting from
unbalanced cluster head distribution as well as to ensure that the energy
consumption across the network is uniform.
1.2.3 Network Scalability
When WSNs is deployed, some time new nodes need to be added to
the network in order to cover more area or to prolong the life time of the current
network. In both the cases the clustering scheme should be able to adapt to
changes in the topology of the network. The key point in designing such
management schemes should be if the algorithm is local and dynamic it will be
easy for it to adapt to topology changes.
1.2.4 Uniform Energy Consumption
Transmission is more energy consuming compared to sensing and
computation in WSNs, therefore the cluster heads which performs the function of
transmitting the data to the base station consume more energy compared to the
rest of the nodes. Clustering schemes should ensure that energy dissipation
across the network should be uniform and the cluster head should be rotated in
order to balance energy consumption across the network.
1.2.5 Multi-Hop or Single Hop Communication
The communication model that wireless sensor networks use is either
single hop or multi-hop. Since energy consumption in wireless system is directly
proportional to the square of the distance, single hop communication is expensive
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in terms of energy consumption. Most of the routing algorithms use the multi-
hop communication model because it is more energy efficient in terms of energy
consumption. However, with multi-hop communication the nodes which are
nearer the cluster head are under heavy traffic and can create holes near the
cluster head.
1.2.6 Attribute Based Addressing
The sheer number of nodes it is not possible to assign unique IDs to
nodes in WSNs. Data is accessed from nodes via attributes and not by IDs. This
makes intrusion into the system easier and implementing a security mechanism
difficult.
1.2.7 Cluster Dynamics
Cluster dynamics means how the different parameters of the cluster
are determined for example, the number of clusters in a particular network. In
some cases the number is pre-assigned and in some cases it is dynamic. The
cluster head performs the function of compression as well as the transmission of
data. The distance between the cluster heads is a major issue. It can be dynamic
or can be set in accordance with some minimum value. In case of dynamic then
there is a possibility of forming unbalanced clusters. While limiting it by some
pre-assigned minimum distance can be effective in some cases but this is an open
research issue. Also, cluster head selection can either be centralized or
decentralized. Both have advantages and disadvantages. The number of clusters
might be fixed or dynamic. Fixed number of clusters cause less overhead and the
network will not have to go again and again through the set up phase in which
clusters are formed. In terms of scalability the fix clustering scheme is not
feasible.
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Figure.1.2 Wireless sensor network
1.3 APPLICATIONS OF HETEROGENEOUS WIRELESS SENSOR NETWORK
1.3.1 Disaster Relief Applications
A typical scenario is wildfire detection.Sensor nodes are equipped
with thermometers and determine their own location. sensors are deployed over
a wildfire (from aeroplane). They collectively produce a “temperature map” of
the area disaster relief applications and have commonalities with military
applications. In such an application, sensors should be cheap enough to be
considered disposable since a large number is necessary and lifetime
requirements are not particularly high.
1.3.2 Environment Control and Biodiversity Mapping
WSNs can be used to control the environment. A possible application
is garbage dump sites Another example is the surveillance of the marine ground
floor. An understanding of its erosion processes is important for the construction
of offshore wind farms. WSNs to gain an understanding of the number of plant
and animal species that live in a given habitat. An advantages of WSNs here are
the long-term, unattended, wire free operation of sensors close to the objects that
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have to be observed. The sensors can be made small enough to be unobtrusive,
they only negligibly disturb the observed animals and plants.
1.3.3 Intelligent Building
Buildings waste vast amounts of energy by inefficient Humidity,
Ventilation, Air Conditioning (HVAC) usage. Real-time, high-resolution
monitoring of temperature, airflow, humidity, and other physical parameters-
required. WSN can considerably increase the comfort level of inhabitants and
reduce the energy consumption. Improved energy efficiency as well as improved
convenience are some goals of “intelligent buildings. Sensor nodes can be used
to monitor mechanical stress levels of buildings. Mechanical parameters like the
bending load of girders, it is possible to quickly ascertain via a WSN whether it
is still safe to enter a given building after an earthquake. Other types of sensors
might be geared toward detecting people enclosed in a collapsed building and
communicating such information to a rescue team.
1.3.4 Facility Management
WSNs have a wide range of possible applications- management of
facilities larger than a single building. Examples - keyless entry
applications,where people wear badges that allow a WSN to check which person
is allowed to enter which areas of a larger company site. An extended to the
detection of intruders, for example of vehicles that pass a street outside of
normal business hours. Widearea WSN could track such a vehicle’s position and
alert security personnel. WSN could be used in a chemical plant to scan for
leaking chemicals. These applications combine challenging requirements as the
required number of sensors can be large and they should be able to operate a
long time on batteries.
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1.3.5 Machine Surveillance and Preventive Maintenance
Idea is to fix sensor nodes to difficult to reach areas of machinery
where they can detect vibration patterns. Examples for such machinery could be
robotics or the axles of trains. Advantage of WSNs - cable free operation,
avoiding a maintenance problem in itself. Wired power supply may or may not
be available depending on the scenario. If it is not available, sensors should last
a long time on a finite supply of energy since exchanging batteries is usually
impractical and costly.
1.3.6 Precision Agriculture
WSN to agriculture allows precise irrigation and fertilizing by
placing humidity/soil composition sensors into the fields. A relatively small
number is claimed to be sufficient, about one sensor per 100 m × 100 m area.
Similarly, pest control can profit from a high-resolution surveillance of farm
land. The livestock breeding can benefit from attaching a sensor to each pig or
cow, which controls the health status of the animal raises alarms if given
thresholds are exceeded.
1.3.7 Medicine and health care
WSN in health care applications is a potentially very beneficial.
Possibilities range from postoperative and intensive care, where sensors are
directly attached to patients. Patient and doctor tracking systems within hospitals
can be literally life saving.
1.3.8 Telematics
This is partially related to logistics applications. where sensors
embedded in the streets or roadsides can gather information about traffic
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conditions. Also interact with the cars to exchange danger warnings about road
conditions or traffic jams ahead.
1.3.9 Logistics
Sensors are requires for several different logistics applications. It is
conceivable to equip goods (individual parcels, for example) with simple
sensors - allow a simple tracking of these objects during transportation. It
facilitate inventory tracking in stores or warehouses. In these applications, there
is often no need for a sensor node to actively communicate. Passive readout of
data is often sufficient. Passive readout is much simpler and cheaper than the
active communication and information processing. RFID (Radio Frequency
Identification) tag cannot support more advanced applications.
1.3.10 Forest Fire Detection
A sensor network is more feasible as an early warning system for
forests. Carefully placing nodes close to vulnerable areas such as hilltops is an
important event. Reducing the number of sensors required to cover a large
geographic area and an important aspect is lifetime. Sensors must operate for a
very long period of time to discover a comparatively rare event.
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CHAPTER 2
LITERATURE REVIEW
2.1 SURVEY OF CLUSTERING TECHNIQUES IN HWSN
There are two types of energy efficient clustering schemes for
WSNs. The clustering schemes used in homogeneous wireless sensor networks
are called homogeneous clustering schemes, and the clustering schemes used in
heterogeneous wireless sensor networks are called heterogeneous clustering
schemes. The first clustering scheme is Low energy adaptive clustering hierarchy
(LEACH), which play a great role in WSNs to enhance the network life time and
improve the power utility. In LEACH cluster is elected in random manner to
achieve the energy distribution. However, the node consumes more power while
transmitting data from node to base station. LEACH performs well under
homogeneous network, but it fails in heterogeneous WSN because the low-
energy nodes will die more rapidly than high-energy nodes.
2.1.1 Hybrid Energy Efficient Distributed Clustering (HEED) [3]
O.Younis et al. (2004), designed to select different cluster heads in a
field according to the amount of energy that is distributed in relation to a
neighboring node. In HEED sensors are quasi-stationary and links between nodes
are symmetric. Energy consumption is non-uniform among all nodes. Same or
different power levels are used for intra-cluster communication. HEED
distribution of energy extends the lifetime of the nodes within the network thus
stabilizing the neighboring node. Only two level hierarchies provided but can be
extended to multilevel hierarchy.
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2.1.2 Distributed Energy Balance Clustering (DEBC) [11]
X.Wang et al. (2007), proposed a protocol for heterogeneous
wireless sensor networks Cluster heads are selected by a probability depending
on the ratio between remaining energy of node and the average energy of
network. The high initial and remaining energy nodes have more chances to be
the cluster heads than the low energy nodes. This protocol also considers two-
level heterogeneity and then it extends the results for multi-level heterogeneity.
DEBC is different from LEACH, which make sure each node can be cluster head
in each n=1/pr.
2.1.3 Cluster Based Service Discovery for Heterogeneous Wireless Sensor Networks [8]
Marin et al. (2008), proposed an energy efficient cluster based
service discovery protocol (C4SD) for HWSNs. The problem addressed in this
technique is to design a service discovery protocol that is suitable for
heterogeneous WSNs and reduces the workload of the resource constrained
devices. Authors proposed a cluster based solution, where a set of nodes are
selected, based on their capabilities. In this algorithm each node is assigned a
unique hardware identifier and weight. Higher the capability grade more
suitability for CH role. These nodes act as a distributed directory of service
registrations for the nodes in the cluster. Since the service discovery messages
are exchanged only among the directory nodes and the distribution of workload
according to the capabilities of the nodes, the communication costs are reduced.
The proposed clustering algorithm reacts rapidly to topological changes of the
sensor network by making decisions based only on the single-hop neighborhood
information, avoids chain reactions and constructs a set of sparsely distributed
CHs. The clustering algorithm is simulated and compared with distributed
mobility adaptive clustering (DMAC). The result shows that it out performs
DMAC.
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2.1.4 Distributed Data Aggregation Energy Efficient Clustering (DDAEEC)
[12]
Kumar, et al. (2009), proposed DDAEE clustering protocol which fit
for the multilevel heterogeneous wireless sensor networks. Here, all the nodes
use the initial and residual energy level to define the cluster heads. It does not
require any global knowledge of energy at every election round. This DDAEEC
algorithm allows a more number of data to send from cluster head to a base
station in a certain time interval. It improves the life time of WSNs.
2.1.5 Stable Election Protocol (SEP) [3]
Smaragdakis et al. (2004), proposed SEP is based on weighted
election probabilities of each node to become cluster head according to the
remaining energy in each node. SEP, for electing cluster heads in a distributed
fashion in two-level hierarchical wireless sensor networks. SEP is heterogeneous-
aware, in the sense that election probabilities are weighted by the initial energy of
a node relative to that of other nodes in the network. This prolongs the time
interval before the death of the first node (stability period), which is crucial for
many applications where the feedback from the sensor network must be reliable.
2.1.6 Novel Stable Selection and Reliable Transmission Protocol for Clustered HWSN [5]
H. Zhou et al. (2008), proposed a model of energy and computation
heterogeneity for heterogeneous wireless sensor networks. They also propose a
protocol named Energy Dissipation Forecast and Clustering Management
(EDFCM) for HWSNs. This algorithm balances the energy consumption round
by round, which will provide the longest stability period for network. The
heterogeneous model they consider is composed of three types of nodes
including Type_0, Type_1 and some management nodes. Type_0 and Type_1
nodes vary in capabilities of sensing, energy and software. They have the
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responsibility of sensing events, while the management nodes perform
management of both types of nodes during cluster formation. EDFCM is
specially proposed for heterogeneous networks to provide the longer lifetime and
more reliable transmission service.
Unlike the other energy efficient protocols, the process of cluster
head selection in EDFCM is based on a method of one step energy consumption
forecast. It uses the average energy consumptions of the two types of cluster
heads in previous round for this purpose. The more remaining energy in a node
after the operation of next round, higher the chances of node to be selected as a
cluster head. In EDFCM protocol, the operation of network can be divided into
two phases: cluster formation phase and data collecting phase. Cluster formation
phase of EDFCM is very similar to that of LEACH, but there are two differences:
The selection probability is a weighted function.
It guarantees a stable number of cluster heads each round.
2.1.7 Energy Efficient Heterogeneous Clustered Scheme (EEHC) [9]
Xin, G et al. (2008), EEHC for electing cluster heads in a distributed
fashion in hierarchical wireless sensor networks. The election probabilities of
cluster heads are weighted by the residual energy of a node relative to that of
other nodes in the network. The algorithm is based on LEACH and works on the
election processes of the cluster head in presence of heterogeneity of nodes.
2.1.8 The Steady Clustering Scheme for HWSN [4]
Liaw et al. (2009), proposed a protocol based on SGCH (Steady
Group Clustering Hierarchy). This protocol divides all nodes into groups by
initial energy. This algorithm proceeds in two steps: Grouping stage and data
transmission stage. Groups are generally clusters. In this algorithm, BS
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broadcasts a Group Head Request (GHR) to all nodes. Every node sends back the
acknowledgement (ACK) with ID and initial energy information. BS selects the
group head by sending a Group Head GH message and group ID. Now every
group head finds its member by sending group request message to all nodes.
Following this way, algorithm forms the groups (clusters). Algorithm considers
the multilevel heterogeneity of sensor nodes in terms of energy.
2.1.9 Routing Protocol for Balancing Energy Consumption in HWSN [7]
Li X. et al. (2007), developed and analyzed a protocol based on
residual energy and energy consumption rate (REECR). It presented the protocol
based on the REECR rather than periodic rotation and stochastic election.
REECR protocol was not perfect in balancing the energy and stability of
network, so they proposed a zone based improvement of this REECR protocol,
naming ZREECR (Zone Based Residual Energy and Energy Consumption Rate).
This protocol improves the stability period. The problem that is considered in this
work is that the cluster head may be very near or very far from BS. In such a
case, balancing the energy consumption is a very tough task and leads to
instability.
2.1.10 Base Station Initiated Dynamic Routing Protocol [9]
S. Verma et al. (2008), propose a routing protocol that is based on
clustering and uses heterogeneity in nodes to increase the network lifetime. In
this scheme, some nodes which are stronger than other nodes in terms of power,
computational capability and location awareness, work as the cluster heads. They
forward information to their parents, towards the base station by aggregating all
the information from their clusters members. Assumptions are considered in this
schemes are: All nodes are deployed uniformly in the field and CHs will be
assumed dead only when their energy is very less. There is no collision between
inter cluster and intra cluster communication. Transmission power of the CH is
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adjusted in such a way that only single hop broadcast is possible. In this
algorithm, how far a CH is from the BS, is defined as level. Low level means that
CH is near to the BS and if level is high it means CH is away from BS
accordingly. Data flow will be always from higher level to lower level. Decision
of levels by base station is based on the range of the CH and normal node.
Ranges of all the nodes are enough to ensure the connectivity and coverage. BS
sets its level to zero and broadcasts a packet to initiate the scheme. Base station
mentions that this packet is only for CHs. Since the CHs have different signal
strength from normal nodes, they receive the packet and set their levels
accordingly. When the CHs of first level are selected, they broadcast their level.
CHs at lower level receive the packet according to the signal strength. They
choose their parent from upper level CHs only. This process is repeated again
and again until all CHs are connected. CH now broadcast a message that all
sensor nodes should join the CH according to the RSS (Radio Signal Strength).
Communication between CH and sensing nodes is single hop, while
communication between different CH is multiple hops. All CHs sends their
position, level and energy consumption to the BS at the end of the round. BS then
analyzes the energy consumption of different CH at the same level.
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CHAPTER 3
EXISTING TECHNIQUES
In recent years, researchers have proved that clustering is an efficient
scheme in increasing network lifetime and scalability of WSNs. In clustering
schemes, there are two types of nodes in one cluster, one cluster head (CH) and
several cluster members. Cluster members collect data from the environment
periodically and send the data to the CHs. CHs serve as fusion point for data
aggregation, so that the actual data transmitted to the base station (BS) is
reduced. Clustered WSNs could also be classified as single-hop and multi-hop. In
a single-hop clustered WSN, the sensor nodes communicate directly with the CH
using a single-hop communication. In a multi-hop WSN, the CHs use multi-
hopping to reach the BS. However, multi-hop communication is often required,
when the communication range of the sensor nodes are limited or the monitoring
area is very large. For direct communication, the CHs furthest away from the BS
are the most critical nodes, whereas in multi-hop communication; the CHs closest
to the BS are burdened with a heavy relay traffic load and die first. Therefore
clustering and multi-hop communication are the most efficient routing schemes
in WSNs to balance the relay traffic over the network and effectively overcome
the path loss effects.
In an existing technique, the effect of heterogeneity in terms of
battery energy is studied because the cost of a sensor node is ten times more than
the cost of an embedded battery. An author considered three types of nodes –
normal, advanced and super nodes with different battery energy. In S-EECP, the
CHs are elected based on different weighted probabilities. The weighted
probability is evaluated based on the ratio between residual energy of each node
and average energy of the network. The nodes with high initial and residual
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energy will have more chances to become CHs per round per epoch. In M-EECP,
after the election of CHs, the member nodes communicate directly with the CH
using a single-hop communication. On the other hand, CHs use multi-hopping to
reach the BS.
Three types of sensor nodes deployed uniformly in a square region,
that is, normal nodes and a few super and advanced nodes. Note E0 is the initial
energy of normal nodes. Let m be the fraction of N normal nodes, which own α
times more energy than the normal ones, we refer to these nodes as advanced
nodes. Thus, there are m × N advanced nodes equipped with initial energy E0(1 +
α). The proportion m0 of super nodes among advanced nodes are equipped with β
times more energy than the normal nodes. Thus, there are m × m0 super nodes
equipped with initial energy E0(1 + β). Hence, the total initial energy of the new
heterogeneous network setting is given by the following equation
……..(3.1)
Where S = (α – m0 × (α − β)). All the CHs are elected periodically by
different weighted probability. Each Member node communicates with their
respective CHs by using single-hop communication (i.e. intra-cluster
communication). Then CHs collect the data from the member nodes in their
respective clusters, aggregate it and transmit it to the BS using multi-hop
communication (i.e. inter-cluster communication).
3.1 Network Model
The first state assumption for the sensors in network model:
All the sensor nodes are uniformly dispersed within a square field.
Sensor nodes and the BS are left unattended after deployment.
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Each member node communicates with their respective CHs using
single-hop communication approach and multi-hop communication
approach is adopted for inter-cluster communication.
A WSN consists of heterogeneous nodes in terms of node energy.
All the sensor nodes are of equal significance.
Data aggregation is the most popular process of aggregating the data
from multiple nodes to eliminate redundant transmission in
clustering schemes, in which each CH aggregates the collected data
and transmits the fused data to the BS.
The BS node has a rechargeable battery in comparison with the other
nodes in the network.
3.2 Optimal Clustering
A similar radio model as discussed in for radio hardware energy
dissipation is used here.
Assume that the distance between transmitter and receiver is d, the
energy consumed for transmitting L bits data from transmitter to the receiver is
given by the following equation
……(3.2)
Where Eelec is the amount of energy consumption of the wireless
circuit for sending and receiving data. By equating the two expressions at d = d0,
we have d0 = Both the parameters εfs free space and the εmp multi-
path fading channel models vary according to the distance between a sender and
a receiver. If the distance is less than a threshold d0, the free space (fs) model is
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used; otherwise, the multi path (mp) model is used. The cost of energy required
for receiving the data is given by the following equation
………..(3.3)
Assume area M × M square metres, and N is the number of nodes
uniformly distributed over the square area. The BS is located at the centre of the
network for simplicity. Each non-CH node sends L bits data to the elected CH
node. Thus, the energy dissipated in the CH node during a round is given by the
following equation
……..(3.4)
Where k is the number of clusters, EDA is the processing cost of a bit
report to the BS and dBS is the average distance between a CH and the BS. The
energy used in a non-CH is given by the following equation
……..(3.5)
Where dCH is the average distance between a cluster member and its
CH, which is given by the following equation
……(3.6)
Where, ρ(x, y) is the node distribution. By combining (3.4) and (3.5),
the total energy dissipated during a round is obtained and given by the following
equation
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……(3.7)
By differentiating, Eround with respect to k and equating to zero, the
optimal number of clusters can be evaluated by the following equation
……..(3.8)
If the distance of a significant percentage of nodes to the BS is
greater than d0 then, the same analysis as discussed in,the following equation
…..(3.9)
By using (3.8) and (3.9), the optimal probability of a node to become
a CH, popt, is obtained which can be computed by the following equation
……(3.10)
Substituting (3.6)–(3.9) into (3.7), the energy Eround dissipated during
a round is obtained. The optimal probability of a node to become a CH is very
important because if the clusters are not constructed in an optimal way, the total
energy consumed during a round is increased exponentially.
3.3 CH Election Mechanism
In LEACH, during the set-up phase, each node generates a random
number between 0 and 1. If this random number is less than the threshold value,
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T(s), which is given by (3.11), then the node becomes a CH for the current round.
During each round, new CHs are elected and as a result balanced load energy is
distributed among the CHs and other nodes of the network
……(3.11)
Where popt is the desired percentage of CHs, r is the count of current
round, G is the set of sensor nodes that have not been elected as CHs in the last
1/popt rounds. LEACH is an iterative process and each iteration is referred to as a
‘round’. Here, round r is defined as a time interval where all clusters members
have to transmit to the CH once.
The first improvement in (3.11) is inclusion of the residual energy
level available for node. It can be derived by reducing the threshold, denoted by
(3.11), relative to the ratio between residual energy of each node and average
energy of the network which is given by the following equation
……(3.12)
Where Ei is the current residual energy of each node and Eavg is the
average energy of the network. The average energy is used as the reference
energy for each node. Initially, all the nodes need to know the total energy and
network lifetime which can be determined a priori. Therefore estimate the
average energy Eavg of the network at rth round by the following equation
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……(3.13)
Where R denotes the total number of rounds of the network lifetime,
which means that every node consumes the same amount of energy in each
round. It is assumed that all the nodes die at the same time, R is the total number
of rounds from the network beginning to all the nodes dying. Let Eround denote the
energy consumed by the WSN in each round. Thus, R can be calculated by the
following equation
…….(3.14)
This above modification of the threshold equation has a drawback.
After certain number of rounds the network is stuck, however, there are still alive
nodes available with enough energy to transmit data to the BS. The reason is that
the remaining nodes have a very low energy level which makes the CH threshold
level too low and hence, CHs election process becomes unstable. Further
modification has been done in threshold (3.12) to solve the above problem. It is
expanded by a factor which increases the threshold for any node that has not been
elected as CH for the last 1/pi rounds. Hence, the new modified threshold value is
given by the following equation (see (3.15)) where rs is the number of
consecutive rounds in which a node has not become CH. Hence, the chance of
each type of node to become a CH increases because of a higher threshold value.
In S-EECP and M-EECP, ti is denoted as the rotating epoch. All the nodes cannot
possess the same residual energy when the network evolves. If the rotating epoch
is the same for all the nodes, then the energy will not be well distributed among
the nodes and therefore the low-energy nodes will die more quickly than the
high-energy nodes. To solve this problem, different epochs based on residual
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energy Ei(r) of each node per round are chosen which is given by the following
equation:
………..(3.15)
3.4 Data Communication Phase
Once the clusters are formed and the TDMA schedule is fixed, the
data communication phase can begin. The active sensor nodes periodically
collect the data and transmit it during their allocated transmission time to the CH.
The radio of each non-CH or member node can be turned off until the node’s
allocated transmission time which minimizes energy consumption in these nodes.
The CH node must keep its receiver on to receive all the data from the member
nodes in the cluster. When all the data have been received, the CH nodes
aggregate the data and route the aggregated data packets to the BS via multi-hop
communication approach.
………………(3.16)
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3.5 Traffic Model
The network traffic model depends on the network application and
the behavior of sensed events. The process of data reporting in WSNs is usually
classified into three categories: (i) time driven, (ii) event driven and, (iii) query
driven. In the time driven case, sensor nodes transmit their data periodically to
the BS. Event driven networks are used when it is desired to inform the BS about
the occurrence of an event. In query-based networks, BS sends a request of data
gathering when it is needed. The time driven scenario is the main focus in S-
EECP and M-EECP because in time driven WSNs, nodes take readings of the
environment and report the same data in a periodic way. Thus, the traffic
generated by these WSNs becomes very predictable, and the use of TDMA-based
mechanisms is not only possible, but also recommended from the perspective of
low-energy consumption.
26
CHAPTER 4
PROPOSED TECHNIQUES
There are various approaches proposed for energy efficient
clustering mechanism in heterogeneous wireless sensor networks. In order to
reduce the energy consumption, cluster formation and cluster head selection is
introduced. A distributed energy efficient clustering mechanism required for
cluster formation and energy efficiency. In this paper the proposed new cluster
head selection mechanism to increase the life time of WSN and a reconfiguration
approach to optimize the energy consumption in inter cluster communication.
4.1 Distributed Energy Efficient Clustering Mechanism
There are two phases that are accomplished in DEECM: setup
phase and steady state phase. In the setup phase, cluster head selection and
cluster formation process is completed. In the steady state phase, selected cluster
head is identified and the selected cluster head uses the inter cluster
communication. Owing to randomness property of WSN; cluster can be small or
large in their deployed region. Large sized clusters consumes more energy than
small sized clusters, because it has long intra cluster distance, i.e., the distance
between the cluster members and the cluster head. In this paper, an improvised
technique that uses multi hop communication links between the cluster members
and the cluster heads is proposed here. Moreover, cluster heads communicate
with the base stations in a multi hop manner. So energy is distributed equally
among the nodes and this result in considerable conservation of energy. The
distance based probability is a function of the distance of the normal sensor to the
advanced sensor. The proposed system develop distributed algorithm which
27
addresses the energy-efficient clustering under the joint coverage and routing
constraint.
There are some assumptions for our protocol as given below;
Base station should be known to all cluster members.
Base station should be independent of energy resources.
Sensor nodes are static.
Sensor nodes locations are not known.
4.2 Cluster Head Selection
Cluster head selection and cluster formation processes are
completed in the setup phase. The cluster head aggregate the data and send to
base station.
Figure 4.1 M-DEECM
Each sensor node computes its approximate distance according to
the strength of receiving signals. The sensor nodes elect the cluster head
according to high energy or threshold. At initial, advanced node is elected as a
cluster head which consists of high threshold value. Each sensor nodes chooses a
random number between 0 and 1 individually. If this is lower than the calculated
28
threshold T(i) for node i, then it becomes a cluster head. Each node becomes a
cluster head in one epoch. An epoch is defined as,
epoc h= 1Popt
………….(4.1)
In the three level heterogeneous nodes (normal nodes, advanced
nodes, and super nodes), a reference value Popt has been replaced by weighted
probabilities. The advanced node (m) has α amount of more energy than normal
nodes.
Pnorm= Popt1+αm
…...….. (4.2)
Padvanced= Popt1+∝m
(1+∝) ..…….….(4.3)
Psuper= Popt1+βm
(1+β ) ………….(4.4)
The total initial energy of the new heterogeneous network setting is
given by the following equations,
E total=N × Eo ×(1+m× S ) …………(4.5)
Where,
S = (α – mo × (α – β))
The CHs are elected periodically by different weighted probability.
Each member node communicates with their respective CHs by using, multi-hop
communication (i.e. inter-cluster communication) when the distance between
normal node to base station is greater. Then CHs collect the data from the normal
29
nodes in their respective clusters, aggregate it and transmit it to the BS using
multi-hop communication.
Assume that the distance between transmitter and receiver is d, the
energy consumed for transmitting L bits data from transmitter to the receiver is
given by the following equation,
Etx ( L, d )=¿L × Ed+L× ɛmp × d4
L × Eelec+L ×ɛ fs ×d 2
¿ …………… (4.6)
Where, Eelec is the amount of energy consumption of the wireless
circuit for sending and receiving data. The energy require for receiving the data is
given by the following equation,
Erx = L × Eelec ……………….(4.7)
The probability of a node to become a cluster head, Popt which can be calculated by the equation,
Kopt=√ N2 π √ ε fs
εmp
M
dBS2 ……………(4.8)
Where, d BS=0.765∗M
2
By using (4.8), the optimal probability of a node to become a
cluster head is constructed. The average energy Eavg of the network at ith,
Eavg ( i )= 1N
E (1− iR
) ………….(4.9)
Where, R is the total number of rounds of the network lifetime.
Therefore, every node consumes the same amount of energy at each epoch.
R=Etotal
E round ………….(4.10)
The average energy for the different epoch based on residual
energy Ei(r) of each node per round can be obtained as,
30
Eavg (r )= 1N∑i=1
N
Ei(r ) …………..(4.11)
4.3 Multi-Hop Communication Mechanism
Two major communication patterns are single-hop and multi-hop
which is mostly used in WSNs. Single-hop communication transmits the data
directly to the BS without any relay, thus the node located away from the BS will
consume more energy than the other nodes and drains faster, die out first. To
conquer this problem, we use shortest path algorithm.
Directed weighted graph for direct communication G = V, E,
where V is a set of nodes and E is a set of edges. Each edge is a pair (v, w),
where v, w V. Edges are sometimes referred to as arcs. If the pair is ordered,
then the graph is directed. Vertex w is adjacent to v if and only if (v, w) E. In
an undirected graph with edge (v, w), and hence (w, v), w is adjacent
to v and v is adjacent to w. Sometimes an edge has a third component, known as
either a weight or a cost.
A cycle in a directed graph is a path of length at least 1 such
that w1 = wn; this cycle is simple if the path is simple. For undirected graphs, we
require that the edges be distinct. The logic of these requirements is that the
path u, v, u in an undirected graph should not be considered a cycle, because
(u, v) and (v, u) are the same edge. In a directed graph, these are different edges,
so it makes sense to call this a cycle. A directed graph is acyclic if it has no
cycles. A directed acyclic graph is sometimes referred to by its
abbreviation, DAG.
An undirected graph is connected if there is a path from every
vertex to every other vertex. A directed graph with this property is called strongly
connected. If a directed graph is not strongly connected, but the underlying graph
(without direction to the arcs) is connected, then the graph is said to be weakly
31
connected. A complete graph is a graph in which there is an edge between every
pair of vertices.
Figure 4.2 Direct Weighted Graphs
4.4 Data Communication Phase
Formerly the clusters are formed and the TDMA plan is set, the
data communication phase can begin. The active node periodically collects and
transmits the data on the basis of allocated time to the CH, where the remaining
node are turned off to minimize the energy consumption. After collecting
information from all the nodes, CH aggregate the data and route that to BS via
multi-hop communication.
32
CHAPTER 5
SOFTWARE DESCRIPTION
5.1 INTRODUCTION TO NETWORK SIMULATOR 2 (NS2)
Network Simulator (Version 2), widely known as NS2, is simply an
event driven simulation tool that has proved useful in studying the dynamic
nature of communication networks. Simulation of wired as well as wireless
network functions and protocols (e.g., routing algorithms, TCP, UDP) can be
done using NS2. In general, NS2 provides users with a way of specifying such
network protocols and simulating their corresponding behaviors. Due to its
flexibility and modular nature, NS2 has gained constant popularity in the
networking research community since its birth in 1989.
Ever since, several revolutions and revisions have marked the
growing maturity of the tool, thanks to substantial contributions from the players
in the field. Among these are the University of California and Cornell University
who developed the REAL network simulator,1 the foundation which NS is based
on. Since 1995 the Defense Advanced Research Projects Agency (DARPA)
supported development of NS through the Virtual Internetwork Tested
(VINT)project .Currently the National Science Foundation (NSF) has joined the
ride in development. Last but not the least, the group of researchers and
developers in the community are constantly working to keep NS2 strong and
versatile.
5.2 BASIC ARCHITECTURE
33
Figure 5.1 shows the basic architecture of NS2. NS2 provides users
with executable command ns which takes on input argument, the name of a Tcl
simulation scripting file. Users are feeding the name of a Tcl simulation script
(which sets up a simulation) as an input argument of an NS2 executable
command ns. In most cases, a simulation trace file is created, and is used to plot
graph and/or to create animation.NS2 consists of two key languages: C++ and
Object-oriented Tool Command Language (OTcl).
While the C++ defines the internal mechanism (i.e.,a backend) of
the simulation objects, the OTcl sets up simulation by assembling and
configuring the objects as well as scheduling discrete events (i.e., a frontend).
The C++ and the OTcl are linked together using TclCL. Mapped to a
C++ object, variables in the OTcl domains are sometimes referred to as handles.
Conceptually, a handle (e.g., n as a Node handle) is just a string (e.g.,_o10) in the
OTcl domain, and does not contain any functionality. Instead, the functionality
(e.g., receiving a packet) is defined in the mapped C++ object (e.g., of class
Connector). In the OTcl domain, a handle acts as a front end which interacts with
users and other OTcl objects. It may defines its own procedures and variables to
facilitate the interaction.
In figure 5.1 is a general user can be thought of standing at the left
bottom corner, designing and running simulations in Tcl using the simulator
objects in the OTcl library. When a simulation is finished,NS produces one or
more text-based output files that contain detailed simulation data,if specified to
do so in the input Tcl script.
34
Figure 5.1 Basic Architecture of network simulator
Note that the member procedures and variables in the OTcl domain
are called instance procedures (instprocs) and instance variables (instvars),
respectively. Before proceeding further, the readers are encouraged to learn C++
and OTcl languages.
NS2 provides a large number of built-in C++ objects. It is advisable
to use these C++ objects to set up a simulation using a Tcl simulation script.
However, advance users may find these objects insufficient. They need to
develop their own C++ objects, and use a OTcl configuration interface to put
together these objects.
After simulation, NS2 outputs either text-based or animation-based
simulation results. To interpret these results graphically and interactively, tools
such as NAM (Network AniMator) and XGraph are used. To analyze a particular
behavior of the network, users can extract a relevant subset of text-based data and
transform it to a more conceivable presentation.
NS2 is an object oriented simulator written in OTcl and C++
languages. While OTcl acts as the frontend (i.e., user interface), C++ acts as the
backend running the actual simulation (Figure 5.2). There are two types of
35
classes in each domain. The first type includes classes which are linked between
the C++ and OTcl domains. In the literature, these OTcl and C++ class
hierarchies are referred to as the interpreted hierarchy and the compiled hierarchy
respectively. The second type includes OTcl and C++ classes which are not
linked together. These classes are neither a part of the interpreted hierarchy nor a
part of compiled hierarchy.
Figure 5.2 C++ and OTcL 5.3 NS2 SIMULATION STEPS
The following steps shows the three key guideline is defining a
simulation scenario in a NS2:
Step 1: Simulation Design
The first step in simulating a network is to design the simulation. In
this step, the users should determine the simulation purposes, network
configuration and assumptions, the performance measures, and the type of
expected results.
Step 2: Configuring and Running Simulation
This step implements the design in the first step. It consists of two phases:
36
• Network Configuration Phase
In this phase network components (e.g.,node, TCP and UDP) are
created and configured according to the simulation design. Also, the events such
as data transfer are scheduled to start at a certain time.
• Simulation Phase
This phase starts the simulation which was configured in the
Network Configuration Phase. It maintains the simulation clock and executes
events chronologically. This phase usually runs until the simulation clock
reached a threshold value specified in the Network Configuration Phase.
In most cases, it is convenient to define a simulation scenario in a Tcl
scripting file (e.g., <file>) and feed the file as an input argument of an NS2
invocation (e.g., executing “ns <file>”).
Step 3: Post Simulation Processing
The main tasks in this step include verifying the integrity of the
program and evaluating the performance of the simulated network. While the
first task is referred to as debugging, the second one is achieved by properly
collecting and compiling simulation result.
The data can be used for simulation analysis or as an input to a
graphical simulation display tool called Network Animator(NAM) that is
developed as a part of VINT project.NAM has a nice graphical user interface
similar to that of a CD player (play, fast-forward, rewind, pause and so on),and
also has a display speed controller. Further, it can graphically present information
such as throughput and number of packet drops at each link, although the
graphical information cannot be used for accurate simulation analysis.
37
4.4 KEY FEATURES
1. Router Queue Management Technique
2. Multicasting
3. Simulation of Wireless Networks
4. Traffic Source Behaviour-CBR, VBR
5. Transport Agents-UDP/TCP
6. Routing
7. Packet Flow
8. Network Topology
9. Applications-Telent,FTP,Ping
10. Tracking Packet on all links/specific links
38
CHAPTER 6
RESULTS AND DISCUSSION
A heterogeneous clustered WSN has been simulated with 100 sensor nodes. The
normal nodes, advanced nodes and super nodes are randomly distributed over the
remote control area. Therefore the horizontal and vertical coordinates of sensor
node is selected randomly. The base station considered to be in corner of the
sensing field.
6.1 SIMULATION WINDOW FOR CLUSTER FORMING PHASE AND
SELECTION OF CLUSTER HEAD
Figure 6.1 Cluster Forming Phase
Three types of nodes are deployed and selected the cluster head
based on the transmission distance and residual energy. In the experiment, the
39
cluster members i.e., normal nodes use multi-hopping to communicate with the
elected CH. CHs use multi-hopping to communicate with the BS.
6.3 SIMULATION RESULT FOR NETWORK LIFETIME
Figure 6.3 Network Lifetime
Network lifetime strongly depends on the lifetimes of single nodes
that constitute heterogeneous WSNs. The definition of network lifetime is
determined by the kind of service it provides. Often, it is necessary that all the
sensor nodes stay alive as long as possible because network performance
decreases as soon as single node dies. In this scenario, it is important to know
when the first node dies.
40
6.4 SIMULATION RESULT FOR PACKET DELIVERY RATIO
Figure 6.3 Packet Delivery Ratios
It defines Number of data packets received at the BS for DEECM
protocol. Therefore DEEM overcomes the imbalance energy consumption
problem by using multi-hop communication among all nodes. It achieves balance
of energy consumption among all nodes and enhances the network lifetime.
41
CHAPTER 7
CONCLUSION
In this work a new clustering based protocol – Distributed energy
efficient clustering mechanism for heterogeneous wireless sensor networks is
obtained. In this protocol, three types of nodes with different battery energy
which is a source of heterogeneity. To suggest an improvised technique that
further enhances the conservation of energy than the existing methods. A new
method is designed, that uses multi hop communication links between the cluster
members and the cluster heads. Further the cluster heads communicate with the
base stations in a multi hop manner. WSN has a rigid requirement regarding its
power consumption due to the limited and non rechargeable energy supply. So by
employing this new improvised technique, the energy is distributed equally
among the nodes and this brings about a considerable conservation of energy.
This method is helpful when the clusters far from base stations need to
communicate with these base stations and they communicate using multi hop
links thereby reducing the energy consumed. Furthermore, the intra cluster
communications using multi hop links distributes the energy among the nodes
within the cluster thereby further reduction of energy consumed. Hence this
method provides an efficient way of conserving energy in wireless sensor
networks and thus increasing the lifetime of wireless sensor nodes within a
network.
42
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