2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 8 , Issue 2 , PP: 36-50, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Rajeev Pandey 1 * , Manoj Kumar 2 , Jaswant Samar 3
Doi: https://doi.org/10.54216/FPA.080204
Wireless sensor networks have made a significant contribution to wireless sensor communication system based on resource constraints and limited computational sensors. Over the last decade, several focused research efforts have been made to investigate and provide solutions to problems relating to the energy efficiency data fusion aggregation in Wireless sensor networks. However, the problem of designing routes that are energy efficient has not been resolved. It is rather a tough effort to guarantee that the lifespan of a sensor is prolonged for a longer period because of the restricted computational capabilities of sensors, which are often coupled with energy constraints. The findings of this work present an enhanced energy-efficient technique for communication in sensor networks which consists of three distinct innovative frameworks. The suggested framework known as Data Fusion with Potential Energy Efficiency (DFWPEE) is responsible for the optimization of energy. The proposed work reduces energy consumption by using probabilistic methods and clustering. During the data fusion process, the Multiple Zone Data Fusion (MZDF) architecture uses a globular topology that helps with load balancing. The strategy presents an innovative routing approach that is used to aid in the performance of energy efficient routing in large-scale wireless sensor networks. By introducing the idea of routing agents, the framework for the Tree-Based Fusion Technique (TBFT), as suggested, comes up with an innovative method for dynamic reconfiguration. The plan enables the system to determine which sensor has a higher rate of energy dissipation and then immediately transfers the job of data fusion to a node that is more energy efficient. This threshold-based technique enables a sensor to perform both the role of a cluster head and the function of a member node. The node behaves as a cluster head until it achieves its threshold remnant energy and functions as a member node after it passes the threshold residual energy. Both of these roles may be played simultaneously. The mathematical modeling was done using the conventional radio energy model which improved the dependability of attained results. The proposed system delivers enhanced energy efficient communication performance when measured against existing implemented standards for energy efficient schemes. The enhanced technique uses nearly half as much energy as LEACH while focusing on reducing the overall time taken for the process to complete leading to enhanced performance.
Tree-Based Fusion Technique , Energy Efficient Data Fusion , Data Aggregation , Wireless Sensor Network
[1] Abbasi Afzaal, A & Asif Kamal, 2011, „An intelligent neural-WSN based schema for energy resources forecast‟, International Journal of Advanced Science and Technology, vol. 33, pp. 121- 130.
[2] Amala Shiny, VA & Nagarajan, V 2012, „Energy Efficient Routing Protocol for Mobile WSN‟, International Journal of Computer Applications (0975 – 8887), April, vol. 43, no. 2, pp. 1-5.
[3] Ashok Kumar, S, Lakshmi Kanthan, N, Sathya Priya, N, Amala, G, Revathi, A & Revathi, R 2014, „A Survey on Flow Balanced Routing in WSNs‟, International Journal Of Technology Enhancements And Emerging Engineering Research, vol. 2, no. 9.
[4] Balamurugan, A 2014, „An energy efficient fitness based routing protocol in WSNs‟, ICTACT Journal on Communication Technology , vol. 5, no. 1, pp. 894-899.
[5] Balavalad, Kirankumar B, Ajayakumar C. Katageri, Poornima M. Chanal & Gururaj Kori 2014, „Energy Efficient Multipath Routing Protocol with Guaranteed Data Delivery for WSN‟,International Journal of Information and Education Technology vol. 4, no.5, pp. 430-435.
[6] Ben-Othman, Jalel & Bashir Yahya 2010, „Energy efficient and QoS based routing protocol for WSNs‟, Journal of Parallel and Distributed Computing, vol. 70, no. 8, pp. 849-857.
[7] Bokare, Madhav & Anagha Ralegaonkar 2012, „WSN: A Promising Approach for Distributed Sensing Task‟, Excel Journal of Engineering Technology and Management Science, vol. 1, no. 1, pp. 1-9.
[8] Boonsawat, Vongsagon, Jurarat Ekchamanonta, Kulwadee Bumrungkhet & Somsak Kittipiyakul 2010, „XBee WSNs for temperature monitoring‟, In the second conference on application research and development (ECTI-CARD 2010), Chon Buri, Thailand. 117 `
[9] Chakraborty, Ayon, Swarup Kumar Mitra & Mrinal Kanti Naskar 2011, „A Genetic algorithm inspired routing protocol for WSNs‟, International Journal of Computational Intelligence Theory and Practice, vol. 6, no. 1, pp. 1-8.
[10] Chaudhary, Sumit, Neha Singh, Avinav Pathak & Vatsa, AK 2012, „Energy Efficient Techniques for Data aggregation and collection in WSN‟, Int. J. Comp. Sci. Eng. Appl. (IJCSEA), vol. 2, no. 4, pp.37-40.
[11] Chen, Rung-Ching, Yung-Fa Haung & Chia-Fen Hsieh 2010, „Ranger intrusion detection system for WSNs with sybil attack based on ontology‟, New Aspects of Applied Informatics, Biomedical Electronics and Informatics and Communications, WSEAS International Conference on Applied Informatics and Communications (AIC '10), pp. 176-180.
[12] Chitra, SM, Vinoba, V & Padmavathy, TY 2013, „Link Reliability Routing Protocol in WSNs using Game Theory Approach‟, In Proceedings of the International Conference on Applied Mathematics and Theoretical Computer Science, pp. 235-238.
[13] Cobb Michael 2009, „Powering High Speed Analog-to-Digital Converters with Switching Power Supplies‟, Power Management Design Line, Analog Devices, May 27.
[14] Elrahim, Adel Gaafar, A, Hussein Elsayed, A, Salwa Ramly, E & Magdy Ibrahim, M 2010, „An energy aware WSN geographic routing protocol‟, Universal Journal of Computer Science and Engineering Technology, vol. 1, no. 2, pp. 105-111.
[15] Enami, Neda, Reza Askari Moghadam, Kourosh Dadashtabar & Mojtaba Hoseini 2010, „Neural network based energy efficiency in WSNs: A survey‟, International Journal of Computer Science & Engineering Survey, vol. 1, no. 1, pp. 39-53.
[16] Fan, GaoJun, and ShiYao Jin 2010, „Coverage problem in WSN: A survey.‟ Journal of networks, vol. 5, no. 9, pp. 1033-1040.
[17] Felemban & Emad 2013, „Advanced border intrusion detection and surveillance using WSN technology‟, Int. J. Communications, Network and System Sciences, no. 6, pp. 251-259, http://dx.doi.org/10.4236 /ijcns. 2013.65028 118
[18] Flouri, K, Beferull-Lozano, B & Tsakalides, P 2006, „Energy-Efficient Distributed Support Vector Machines for Wireless Sensor Networks, in Proc. 2006 European Workshop on Wireless Sensor Networks (EWSN ‟06), Zurich, Switzerland, February 13-15.
