Relevant bibliographies by topics / DDEEC

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'DDEEC'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "DDEEC"

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Anita, Mahato* Kailash Patidar. "A REVIWE ARTICLE OF COMPARITIVELY ANALYSIS OF DEEC PROTOCOL." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 6 (2017): 113–17. https://doi.org/10.5281/zenodo.805385.

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Energy Management can be improved by proficient clustering algorithms in heterogeneous wireless sensor networks. Coordination through cluster head selection provides efficient data aggregation that reduces communication overhead in the network. In this paper, we propose a fuzzy logic approach based DDEEC clustering algorithm which aims to prolong the lifetime of nodes in heterogeneous WSNs. We compare this algorithm with the PSO based DDEEC algorithm and original DDEEC algorithm according to the parameters of first node dies at different rounds and energy-efficiency metrics. The efficiency of
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Alsafi, Sahar, and Samani A. Talab. "Implementation Of DEEC, DDEEC, EDEEC And TDEEC Protocols Using MATLAB In Wireless Sensor Network." International Journal of Advanced Networking and Applications 12, no. 03 (2020): 4596–600. http://dx.doi.org/10.35444/ijana.2020.12304.

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Behera, T. M., S. K. Mohapatra, and O. P. Acharya. "Performance Analysis of Heterogenous WSN for application in IoT." Oriental journal of computer science and technology 10, no. 04 (2017): 730–35. http://dx.doi.org/10.13005/ojcst/10.04.05.

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Extending the Internet to connect any physical device with smart technology can be called as the Internet of Things (IoT).It is a proposed development of the Internet in which everything can be connected to the internet enabling them to send and receive data. Selection of energy efficient routing protocols has become an essential step in designing any IoT network. Moreover, the protocol should also be selected to enhance the lifetime of the network. In this article, we evaluate the performance analysis of DEEC (Distributed Energy Efficient Clustering), DDEEC (Developed DEEC), EDEEC (Enhanced DE
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Ghosh, Ramkrishna, Suneeta Mohanty, Prasant Kumar Pattnaik, and Sabyasachi Pramanik. "A Performance Assessment of Power-Efficient Variants of Distributed Energy-Efficient Clustering Protocols in WSN." International Journal of Interactive Communication Systems and Technologies 10, no. 2 (2020): 1–14. http://dx.doi.org/10.4018/ijicst.2020070101.

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Wireless sensor networks (WSNs) have enabled an improved awareness in a range of purposes like battlefield surveillance, disaster management, industrial process monitoring and control, machine health monitoring, and medical applications. Accordingly, great numbers of sensor nodes (SNs) are arranged for usages that can function as an individual. Because of minute power of batteries in WSNs, proficient exploitation of this power is a significant aspect. Clustering is a capable method to expand the lifespan of WSNs by diminishing the power utilization. In the research work, the authors have compa
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Rathee, Anu, Indu Kashyap, and Kavita Choudhary. "Developed Distributed Energy-Efficient Clustering (DDEEC) Algorithm based on Fuzzy Logic Approach for Optimizing Energy Management in Heterogeneous WSNs." International Journal of Computer Applications 115, no. 17 (2015): 14–19. http://dx.doi.org/10.5120/20241-2594.

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Wang, Jun, Zhuangzhuang Du, Zhengkun He, and Xunyang Wang. "A Cluster-Head Rotating Election Routing Protocol for Energy Consumption Optimization in Wireless Sensor Networks." Complexity 2020 (December 21, 2020): 1–13. http://dx.doi.org/10.1155/2020/6660117.

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Balancing energy consumption using the clustering routing algorithms is one of the most practical solutions for prolonging the lifetime of resource-limited wireless sensor networks (WSNs). However, existing protocols cannot adequately minimize and balance the total network energy dissipation due to the additional tasks of data acquisition and transmission of cluster heads. In this paper, a cluster-head rotating election routing protocol is proposed to alleviate the problem. We discovered that the regular hierarchical clustering method and the scheme of cluster-head election area division had p
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Aroba, Oluwasegun Julius, Nalindren Naicker, and Timothy Adeliyi. "An Innovative Hyperheuristic, Gaussian Clustering Scheme for Energy-Efficient Optimization in Wireless Sensor Networks." Journal of Sensors 2021 (February 10, 2021): 1–12. http://dx.doi.org/10.1155/2021/6666742.

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Energy stability on sensor nodes in wireless sensor networks (WSNs) is always an important challenge, especially during data capturing and transmission of packets. The recent advancement in distributed clustering algorithms in the extant literature proposed for energy efficiency showed refinements in deployment of sensor nodes, network duration stability, and throughput of information data that are channelled to the base station. However, much scope still exists for energy improvements in a heterogeneous WSN environment. This research study uses the Gaussian elimination method merged with dist
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Gagandeep, Kaur. "ENERGY EFFICIENT ROUTING ALGORITHMS FOR SCHEDULING DISTRIBUTION OF CLUSTER HEADS IN WIRELESS SENSOR NETWORKS (WSNS)-A REVIEW." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 8, no. 3 (2019): 252–67. https://doi.org/10.5281/zenodo.5457901.

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The wireless sensor networks (WSNs) operate on resource constrained environment, therefore, changing or recharging the batteries is difficult and unmanageable task. Over the years, several routing algorithms have been studied in WSNs to enhance their lifespan with varying degree of success. The present review compared the energy efficiency of various homogeneous and heterogeneous routing algorithms for scheduling the distribution of cluster heads (CHs) in WSNs. The Low Energy Adaptive Clustering Hierarchy (LEACH), Power Efficient Gathering in Sensor Information Systems (PEGASIS) and Hybrid Ene
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Aroba, Oluwasegun Julius, Nalindren Naicker, and Timothy Adeliyi. "A Hyper-Heuristic Heterogeneous Multisensor Node Scheme for Energy Efficiency in Larger Wireless Sensor Networks Using DEEC-Gaussian Algorithm." Mobile Information Systems 2021 (February 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/6658840.

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A wireless sensor network (WSN) is an intellect-sustainable network that comprises multiple spatially distributed sensor nodes and several sink nodes that collect data from sensors. WSNs remain an active research area in the literature due to challenging factors such as the selection of sensor location according to a given premise, finding optimal routing algorithm, and ensuring energy efficiency and consumption. Minimizing energy and prolonging the network lifetime in the WSNs are the focus of this research work. In the literature, a clustering approach is used in grouping sensor nodes into c
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Zhang, Cangheng, Yan Li, Shu Xing, Xiaodeng Yang, Jinrong Zhao, and Qiaoyan Dong. "Studies on Intermolecular Interaction of N-Glycidyltrimethyl Ammonium Chloride Modified Chitosan/N,N-Dimethyl-N-dodecyl-N-(2,3-epoxy propyl) Ammonium Chloride and Curcumin Delivery." Polymers 14, no. 10 (2022): 1936. http://dx.doi.org/10.3390/polym14101936.

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Chitosan has potential applications in many fields, due to its biocompatibility, biodegradability and reproducibility. However, the insolubility in water restricts its wide application. In order to expand the application of chitosan in the delivery of oil-soluble drugs and improve the efficacy of oil-soluble drugs, N-Glycidyltrimethyl ammonium chloride-modified chitosan (GTA-m-CS) and N,N-Dimethyl-N-dodecyl-N-(1,2-epoxy propyl) ammonium chloride (DDEAC), a kind of reactive surfactant, were synthesized and characterized by FTIR, NMR and XRD methods. The interactions between GTA-m-CS and DDEAC w
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Dissertations / Theses on the topic "DDEEC"

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Valdivia, Karina. "Caractérisation du phénotype associé à la mutation masculinisante "mal" chez la truite arc-enciel, Oncorhynchus mykiss." Phd thesis, Rennes 1, 2012. https://ecm.univ-rennes1.fr/nuxeo/site/esupversions/187e2221-ddee-41d5-8f81-4a7b17551a27.

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La truite arc-en-ciel a un déterminisme du sexe strictement génétique de type XX/XY. Pourtant des femelles génétiques se sont spontanément masculinisées après une gynogénèse endomitotique. Ce phénotype masculinisé à pénétrance incomplète serait dû à une mutation inconnue appelée « mal ». Afin d'identifier le gène causal porteur de cette mutation, une meilleure caractérisation de son phénotype est nécessaire. Pour cela, nous avons procédé à la caractérisation du phénotype gonadique en utilisant à la fois des approches histologiques et moléculaires. Tous les animaux XXmal présentent une différen
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Books on the topic "DDEEC"

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DDEC II diagnostic troubleshooting guide. Detroit Diesel Corp., 1990.

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Gideon, Bush. Zombies Coloring Book: Interesting Coloring Book, Each Page Brings Different Pictures Ddeer to Help You Increase Your Creativity Suitable for Fans of All Ages. 30+ GIANT Great Pages with Premium Quality Images. Independently Published, 2021.

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Gideon, Bush. Zombies Coloring Book: Interesting Coloring Book, Each Page Brings Different Pictures Ddeer to Help You Increase Your Creativity Suitable for Fans of All Ages. 30+ GIANT Great Pages with Premium Quality Images. Independently Published, 2021.

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Book chapters on the topic "DDEEC"

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Mehrotra, Praghya, and Diwakar Bhardwaj. "“Evaluating Energy Efficiency and Performance of WSN Routing Protocols: A Comparative Study of LEACH, DEEC, DDEEC, and EESAA”." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3333-3_8.

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"9 . . TThhee si stiu tu at aito io nnooff fu fu ll l -l t -i t m im e e rrrr   r r      rrrr   r r   ( b ( b , b , b ,  ,  , b , b ) ) = ( a ( a , a , a ,  ,  , a , a ) )      =             r  rrr    r r  (4) (4) TThhee ultimate goal of using the FAHP is to find ultimate goal of using the FAHP is to find oouuttth th eere re la la ti t v iv eewwee ig ig hhttooffeeaacchh ind indic ic a a to to r r to to oovvee ra ra ll l l ggooaallooffAA . . A A ss s u su m m in in ggth th aattth th eewwee ig ig hhttvvaa lu lu eevveecc to to rrooff eeaacchh in in d d ic ic a a to to rroonn th th eemm id id d d le le ti t e ie rrBB re re la la ti t v iv e e to to th th e e oovvee ra ra ll llggooaallooffAAisisaassfo fo ll l o lo wws: s : W W = { { w w , w , w ,. , . . . . . . w .w } } = (5 (5) ) The weight value vector of each indicator on the in in ddeexxla la yyeerrCCre re la la ti t v iv eeto to t h th eemm id id d d le le l a la yyeerrBBisisaassfo fo l-l- lo lo wws: s : (6 (6 ) ) AAnnddth th eewwee ig ig hhttvvaa lu lu eevveecc to to rrooffeeaacchhin in d d ic ic a a to to rroonnTThheeppoo ss sisb ib il iilt ity the index layer C relative to the overall goal of A is y in in fl f u lu eenncceefa fa c c to to rrin in d d ic ic a a to to rs rsaannddth th e e the index layer C relative to the overall goal of A is as follows: ccoonn se se qquueenn ti t a ia llin in fl f u lu as follows: eenncceefa fa c c to to rrin in d d ic ic a a to to rs rsaa re re d d iv iv id id eedd in in to to fo fo uurrdd if iffe fe re re nntt le le vvee ls l , s , eeaacchh le level corre vel corresp sp oonndd in in g g (7 (7 ) ) to to d d if iffe fe re re nnttgg ra ra ddeess ( a (a sssh sh oowwnnin in TTaabb le le 2 2 ). ) . In In t h th is isaa rt ritc ic le le , , t h th eefu fu zzzzyyccoonn si ssitse te nnttju ju ddggmmeennttmmaa --tr tirx ix eessoofffa fa il i u lu re re p p ro ro bbaabb il iiltiy ty aannddfa fa il i u lu re re c con onse se qquueennccee Table 2. The Indicator Score Table of the Mobile a a re re e e st s a ta b b li lsih sh eeddbbaa se se ddoonnth th eefu fu zzzzyyccoonn si ssitse te n nt matrix Table 2. The Indicator Score Table of the Mobile t matrix Crane Risk Factors bbyyPP ro ro fe fe ss s o so r r Ji J a ia nnggLL ia ia nnggkkuui. i . AAccccoo rd rd in in g g to to th the Cr Fv a tors wwee ig ig hhttvvaa lu lu eeccaa lc lc u u la la ti t o io nnmmee th th ooddoofffu fu zzzzyyccoonn si ssitse te nntt matrix matrix bbyyPP ro ro fe fe ss s o so rrLLvvYYuuee ji j n in , , f u fu zzzzyyccoonn si ssitse te nnttmmaa --tr tirx ix wwee ig ig h h ts tsWWccaannbbeeeexxpp re re ss s e se ddaassfo fo ll l o lo wws: s : 3 Determination of Index Weight (8 (8) ) AAccccoo rd rd in in g g to to th th eeFFAAHHPP , , wwee ig ig h h ts tsooffpp ro ro bbaabb il ili The FAHP ity ty The FAHP isisuu se se ddto to ddee te te rm rm in in eeth th eein in ddeexxwwee ig ig hht. t . A A indicators and consequential indicators are factor set is: U = {u , u , …, u }. The evaluation set indicators and consequential indicators are oobb ta ta in in eedd , , factor set is: U = {u, u, …, u}. The evaluation set as shown in Table 3 and Table 4." In Structural Health Monitoring and Integrity Management. CRC Press, 2015. http://dx.doi.org/10.1201/b18510-131.

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Conference papers on the topic "DDEEC"

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Elbhiri, Brahim, Rachid Saadane, Sanaa El fldhi, and Driss Aboutajdine. "Developed Distributed Energy-Efficient Clustering (DDEEC) for heterogeneous wireless sensor networks." In 2010 5th International Symposium On I/V Communications and Mobile Network (ISVC 2010). IEEE, 2010. http://dx.doi.org/10.1109/isvc.2010.5656252.

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Hames, R. J., R. D. Straub, and R. W. Amann. "DDEC Detroit Diesel Electronic Control." In SAE International Congress and Exposition. SAE International, 1985. http://dx.doi.org/10.4271/850542.

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Hames, Richard J., David L. Hart, Gregory V. Gillham, Steve M. Weisman, and Bernd E. Peitsch. "DDEC II-Advanced Electronic Diesel Control." In International Congress on Transportation Electronics. SAE International, 1986. http://dx.doi.org/10.4271/861049.

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Hames, Richard J., David L. Hart, Gregory V. Gillham, Steve M. Weisman, and Bernd E. Peitsch. "DDEC II Advanced Electronic Diesel Control." In West Coast International Meeting and Exposition. SAE International, 1986. http://dx.doi.org/10.4271/861110.

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Brown, Lester W. Walker Raymond R., and Christine A. Burkel Waidrop L O'Donnell. "Reliability Engineering Program for the Detroit Diesel Electronic Control (DDEC)." In SAE International Congress and Exposition. SAE International, 1988. http://dx.doi.org/10.4271/880490.

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Kaur, Prabhleen, and Rajdeep Singh. "DDEC: Distance based deterministic energy efficient clustering protocol for wireless sensor networks." In 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2014. http://dx.doi.org/10.1109/icacci.2014.6968375.

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Daily, Jeremy, Andrew Kongs, James Johnson, and Jose Corcega. "Extracting Event Data from Memory Chips within a Detroit Diesel DDEC V." In SAE 2015 World Congress & Exhibition. SAE International, 2015. http://dx.doi.org/10.4271/2015-01-1450.

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