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Project 1: ANALYSIS OF LEACH PROTOCOL
As we all know that all the networks have a certain lifetime during
which nodes have limited energy by using that, the nodes gather,
process, and transmit information. This means that all aspects of the
node, from the sensor module to the hardware and protocols, must be
designed to be extremely energy efficient. Decreasing energy usage by a
factor of two can double system lifetime, resulting in a large increase in
the overall usefulness of the system. In addition, to reduce energy
dissipation, protocols should be robust to node failures, fault tolerant and
scalable in order to maximize system lifetime.
LEACH is the first network protocol that uses hierarchical routing for
wireless sensor networks to increase the life time of network. All the
nodes in a network organize themselves into local clusters, with one
node acting as the cluster head. All non-cluster head nodes transmit their
data to the cluster - head, while the cluster - head node receive data from
all the cluster members, perform signal processing functions on the data
(e.g., data aggregation), and transmit data to the remote base station.
Therefore, being a cluster head node is much more energy intensive than
being a non cluster head node. Thus, when a cluster head node dies all
the nodes that belong to the cluster lose communication ability.
Project 2: ENERGY OPTIMIZATION OF WSN USING GENETIC
ALGORITHM
Finding appropriate cluster heads is critically important for minimizing
the distance. We use binary representation in which each bit corresponds
to one sensor or node. “1” means that corresponding sensor is a cluster-
head; otherwise, it is a regular node. The initial population consists of
randomly generated individuals. GA is used to select cluster-heads.
To minimize the energy consumption in Wireless Sensor Networks by
optimizing the communication distance between the sensor-nodes and
the sink-point. To compare the number of cluster heads between GA
method and LEACH protocol.
Project3: PROJECT BASED ON MOBILE ADHOC NETWORK(MANET)
Mobile Ad-hoc Networks (MANET) are created of composite
distributed systems which connect wireless nodes. These nodes can
generously and dynamically self in mobile network topologies. Since the
nodes are mobile, the network topology may change rapidly and
unpredictably over time. The network is decentralized, where all
network activity including discovering the topology and delivering
messages must be executed by the nodes themselves, i.e., routing
functionality will be incorporated into mobile nodes. MANETs need
efficient distributed algorithms to determine network organization, link
scheduling, and routing. However, determining viable routing paths and
delivering messages in a decentralized environment where network
topology fluctuates is not a well-defined problem. While the shortest
path (based on a given cost function) from a source to a destination in a
static network is usually the optimal route, this idea is not easily
extended to MANETs. Factors such as variable wireless link quality,
propagation path loss, fading, multiuser interference, power expended,
and topological changes, become relevant issues. The network should be
able to adaptively alter the routing paths to alleviate any of these effects

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Project names for b.tech

  • 1. Project 1: ANALYSIS OF LEACH PROTOCOL As we all know that all the networks have a certain lifetime during which nodes have limited energy by using that, the nodes gather, process, and transmit information. This means that all aspects of the node, from the sensor module to the hardware and protocols, must be designed to be extremely energy efficient. Decreasing energy usage by a factor of two can double system lifetime, resulting in a large increase in the overall usefulness of the system. In addition, to reduce energy dissipation, protocols should be robust to node failures, fault tolerant and scalable in order to maximize system lifetime. LEACH is the first network protocol that uses hierarchical routing for wireless sensor networks to increase the life time of network. All the nodes in a network organize themselves into local clusters, with one node acting as the cluster head. All non-cluster head nodes transmit their data to the cluster - head, while the cluster - head node receive data from all the cluster members, perform signal processing functions on the data (e.g., data aggregation), and transmit data to the remote base station. Therefore, being a cluster head node is much more energy intensive than being a non cluster head node. Thus, when a cluster head node dies all the nodes that belong to the cluster lose communication ability. Project 2: ENERGY OPTIMIZATION OF WSN USING GENETIC ALGORITHM Finding appropriate cluster heads is critically important for minimizing the distance. We use binary representation in which each bit corresponds to one sensor or node. “1” means that corresponding sensor is a cluster- head; otherwise, it is a regular node. The initial population consists of randomly generated individuals. GA is used to select cluster-heads.
  • 2. To minimize the energy consumption in Wireless Sensor Networks by optimizing the communication distance between the sensor-nodes and the sink-point. To compare the number of cluster heads between GA method and LEACH protocol. Project3: PROJECT BASED ON MOBILE ADHOC NETWORK(MANET) Mobile Ad-hoc Networks (MANET) are created of composite distributed systems which connect wireless nodes. These nodes can generously and dynamically self in mobile network topologies. Since the nodes are mobile, the network topology may change rapidly and unpredictably over time. The network is decentralized, where all network activity including discovering the topology and delivering messages must be executed by the nodes themselves, i.e., routing functionality will be incorporated into mobile nodes. MANETs need efficient distributed algorithms to determine network organization, link scheduling, and routing. However, determining viable routing paths and delivering messages in a decentralized environment where network topology fluctuates is not a well-defined problem. While the shortest path (based on a given cost function) from a source to a destination in a static network is usually the optimal route, this idea is not easily extended to MANETs. Factors such as variable wireless link quality, propagation path loss, fading, multiuser interference, power expended, and topological changes, become relevant issues. The network should be able to adaptively alter the routing paths to alleviate any of these effects