Channakrishna raju

Work place: Sri Siddhartha University, Tumkur, India



Research Interests: Computer systems and computational processes, Artificial Intelligence, Computer Architecture and Organization, Computer Networks, Data Structures and Algorithms


Mr. Channakrishna raju has 15 years of teaching experience for UG and PG courses in computer Science and Engg.,.and is presently working as Assistant Professor in the department of computer Science and Engg., in Sri Siddhartha Institute of Technology, Tumkur. He Obtained B.E from Bangalore University in the year 1995 and PG in software systems in the year 2000 from BITS Pilani. His research interests are in the areas of Wireless sensor networks and Artificial Intelligence.. Currently pursuing doctoral degree in Sri Siddhartha University, Tumkur, under the guidance of Dr. Siddappa M, Professor and head of the department of Computer Science and Engineering, Sri Siddartha Institute of Technology, Tumkur.

Author Articles
Survey on an Efficient Coverage and Connectivity of Wireless Sensor Networks using Intelligent Algorithms

By M.Siddappa Channakrishna raju

DOI:, Pub. Date: 8 May 2012

Wireless sensor networks are often deployed for data-gathering or monitoring in a geographical region. This paper explains an important issue to maintain the fidelity of the sensed data while minimizing energy usage in the network. Nature inspired computation like evolutionary computation, swarm intelligence etc., which offers practical advantages to the researcher facing difficult optimization problems. The genetic algorithms are used for efficient connectivity and coverage. Single Objective Genetic Algorithms (SOGA) method is used to yield good results in terms of Coverage, but the objective’s graph had shown Pareto optimal designs with differing Endurance. However it is attractive to offer Pareto optimal designs to a user willing to settle for a poorer Coverage in order to gain in Endurance, so that the sensor network lasts longer. This explains concept of Multiple Objective Genetic Algorithm (MOGA) and its implementation and results which are compared to those of the SOGA. Endurance and Robustness to deployment inaccuracy tend to work in the same direction. A MOGA was conducted with the Coverage and Robustness as objectives. The main objective of this paper is to propose new Strength Perito Evolutionary Algorithm (SPEA) method along with clustering, this will reduce the distances between the sensor nodes that increase the efficiency of the nodes and also increase the connectivity. This will increase lifetime of sensors and connectivity.

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