Abstract
Wireless sensor network is now being increasingly popular in today’s world and is getting used in many kinds of fields and applications where different protocols are being used for the better communication. There is also a great requirement for the improvement of different parameters related to the different issues in this domain. This paper specifically discuss the performance related to increasing the energy efficiency of the hybrid protocol. In new hybrid protocol design which will increases life and energy efficacy of the network using the specifications of the various network QoS tools more particularly system concerns with network simulator NS2 and different parameters and issues regarding the same.
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References
Agrawal N, Singhai J, Agarwal DK (2017) Grape leaf disease detection and classification using multi-class support vector machine. In: International conference on recent innovations in signal processing and embedded systems (RISE). IEEE
Dasgupta SR et al (2020) Detection of diseases in potato leaves using transfer learning. Comput Intell Pattern Recogn 675–684. Springer , Singapore
Dhingra G, Kumar V, Joshi HD (2018) Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools Appl 77:1519951–20000
Ferentinos, Konstantinos (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318
Hlaing CS, Zaw SMM (2018) Tomato plant diseases classification using statistical texture feature and color feature. In: IEEE/ACIS 17th international conference on computer and information science (ICIS). IEEE
Iqbal Z et al (2018) An automated detection and classification of citrus plant diseases using image processing techniques: a review. Comput Electron Agric 153
Islam M et al (2017) Detection of potato diseases using image segmentation and multiclass support vector machine. In: IEEE 30th Canadian conference on electrical and computer engineering (CCECE). IEEE
Jiang P et al (2019) Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access 7
Khan MA et al (2019) An optimized method for segmentation and classification of apple diseases based on strong correlation and genetic algorithm based feature selection. IEEE Access
Kour VP, Arora S (2019) Particle swarm optimization based support vector machine (P-SVM) for the segmentation and classification of plants. IEEE Access 7
Kamavisdar P, Saluja S, Agrawal S (2013) A survey on image classification approaches and techniques. Int J Adv Res Comput Commun Eng
Liu B et al (2018) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry10(1)
Lin Z et al (2019) A unified matrix-based convolutional neural network for fine-grained image classification of wheat leaf diseases. IEEE Access 7
Ma J et al (2018) A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Comput Electron Agric 54
Ogundile OO, Balogun MB, Ijiga OE, Falayi EO (2019) Energy-balanced and energy-efficient clustering routing protocol for wireless sensor networks. IET Commun 13(10):1449–1457
Elsmany EFA, Omar MA, Wan TC, Altahir AA (2019) EESRA: energy efficient scalable routing algorithm for wireless sensor networks. IEEE Access 7:96974–96983
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Pethe, R., Mahkalkar, N. (2023). Analysis of Energy Efficient Routing Protocol for Wireless Sensor Network in Environmental Monitoring. In: Fong, S., Dey, N., Joshi, A. (eds) ICT Analysis and Applications. Lecture Notes in Networks and Systems, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-19-5224-1_12
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DOI: https://doi.org/10.1007/978-981-19-5224-1_12
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