Geographic Routing In WSN for Measuring Coverage Constraints and Energy Consumption in Cloud Environments
K. V. Nithya1, V. Umadevi2

1K.V.Nithya*, Research Scholar, Department of PG & Research in Computer Science, Jairams Arts and Science College, (Affiliated to Bharathidasan University) Karur, Tamil Nadu, India.
2Dr.V.Umadevi, Research Supervisor, Department of PG & Research in Computer Science, Jairams Arts and Science College (Affiliated to Bharathidasan University) Karur. Tamil Nadu, India.
Manuscript received on December 17, 2019. | Revised Manuscript received on December 25, 2019. | Manuscript published on January 10, 2020. | PP: 3069-3072 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8438019320/2020©BEIESP | DOI: 10.35940/ijitee.C8438.019320
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: This paper investigates the problem of energy efficient and consumption process in wireless sensor networks. The fundamental problem in WSN has handled the energy constraints and avoiding end-to-end delay networks. We introduce encoding technique for handling transmission data, network delays, content aware service and energy expenditure problems. The end-to-end delay varies depending up on nodes and sensor life time. We apply hamming encoding technique for estimating energy constraints with life time of buffer capacity factor. This paper proposes the novel based approach to handle group communication, senor node status, network transmission and optimal encoded behaviors. Each cluster group values are sensed by node reference time and calculate hamming code weight for each node counts. The quality of services can be achieved by energy saving measurements and data loss can be varies up on incoming packet request. We compare the encoded result with code word situation and hazard environment. We reduce the amount of data transmission factor and life time of sensor values and longer network delays. We show the simulating results with encoding scheme and reduced energy consumption. The performance can be monitored by coverage constraints and optimal transmission behaviors. 
Keywords:  Coverage Constraints, Data Loss, Energy Consumption, Hamming Encoding, Wireless Sensor Networks
Scope of the Article: Software Engineering Tools and Environments