Chiranjib Patra

Using self organizing map in wireless sensor network for designing energy efficient topologies

There are many challenges involving WSN design such as the energy resources optimization, the robustness and the network coverage. We address here the problem of energy-efficient topology design. A well designed dynamic topology and efficient routing algorithms may allow a large reduction on the energy consumption. As such type of network, the sensors are connected in an ad-hoc manner, without any deterministic way. This paper is concerned with applying standard routing protocols into wireless sensor network by using topology modified by Neural Network which proves to be energy efficient as compared with unmodified topology. Neural Network has been proved to be a powerful tool in the distributed environment. Here, to capture the true distributed nature of the Wireless Sensor Network (WSN), neural networks Self organizing Feature Map (SOFM) is used.

Analysing topology control protocols in wireless sensor network using network evolution model

In the study of wireless ad hoc and sensor networks, clustering is an important research problem as it aims at maximizing network lifetime and minimizing latency. A large number of algorithms have been devised to compute “good” clusters in a WSN but few papers have tried to characterize these algorithms in an analytical manner. In this paper, we use a local world model to understand and characterize the functioning of three tree based clustering algorithms. In particular, we have chosen simple tree, CDS Rule K, and A3 topology construction protocols. Using our theoretical framework based on a complex network model, we have also tried to quantify some of the observed features of these algorithms such as number of cluster heads and average degree of the resultant graph. The theoretically obtained measures have reasonably matched with measures obtained by simulation studies.

Designing Energy-Efficient Topologies for Wireless Sensor Network: Neural Approach

Preserving energy or battery power of wireless sensor network is of major concern. As such type of network, the sensors are deployed in an ad hoc manner, without any deterministic way. This paper is concerned with applying standard routing protocols into wireless sensor network by using topology modified by neural network which proves to be energy efficient as compared with unmodified topology. Neural network has been proved to be a powerful tool in the distributed environment. Here, to capture the true distributed nature of the Wireless Sensor Network (WSN), neural networks Self-Organizing Feature Map (SOFM) is used.

An Energy Efficient Event Based Hierarchical Clustering Algorithm for Wireless Sensor Networks

WSN’s present a new challenge in current research works due to their unlimited potential. Sensor nodes operate with limited battery power, which is seldom replaced or recharged. So, energy utilization is an important constraint in designing routing protocols in WSN. In this paper, we proposed an event based hierarchical cluster based routing algorithm with the objective of reducing the energy consumption for creating the routing topology and thus maximize the lifetime of Wireless Sensor Networks. The performance of the proposed protocol has been examined and evaluated through a simulation study. The simulation results clearly show that it has a better performance than EECDS.

A reliable two-tier energy-efficient topology building algorithm for Wireless Sensor Networks

In spite of various constraints present in Wireless Sensor Networks (WSN), it has become popular in various application domains which need useful information. Among these constraints, energy conservation is the most important aspect. One of the known strategies to save energy and to prolong the lifetime of the WSNs is topology control. In this paper, a reliable energy-efficient topology control algorithm in wireless sensor networks is proposed. The proposed algorithm considers the residual energy and number of neighbors of each node for cluster formation, which is critical for well-balanced energy dissipation of the network. Knowledge-based inference approach is employed to select cluster head. Finally, reliable cluster heads are selected that maintain connectivity and coverage with respect to time. The algorithm not only balances the energy load of each node but also provides global reliability for the whole network. Simulation results demonstrate that the proposed algorithm effectively prolong the network lifetime and reduces the energy consumption.