Archive

Abstract:

Mobile Ad-Hoc Network is a decentralized organization that operates without a foundation. Due to the Mobile Ad-Hoc Network's self-configurable and simple organizational component, many applications may be run. Because of this, various applications are available. If helpful guidelines are established, Mobile Ad-Hoc Network will become dependable. We shall research the organization's competent steering convention for hypertext transfer protocol traffic. We must reach this conclusion with accuracy since this will be the main focus of our inquiry. Latency and throughput were employed to achieve show research goals. For this work reenactment inquiry, your expectations must be based on the conventions it chose since they performed better on all four perspectives. After analyzing its needs, an organization may improve its operations by choosing better conventions. This may boost an organization's efficiency. This research examined AODV, DSR, and OLSR routing methods. This study used OPNET Modeler 14.5 to enhance ad-hoc network performance.

Keywords:

AODV,DSR,Opnet,OLSR,Routing protocols,

Refference:

I. AbdulAmeer, Sabah Auda, et al. “Cyber Security Readiness in Iraq: Role of the Human Rights Activists.” International Journal of Cyber Criminology 16.2 (2022): 1-14.
II. Adhvaryu, K. ” Performance comparison of multicast routing protocols based on route discovery process for MANET ” . In Inventive Communication and Computational Technologies (pp. 79-85). Springer, Singapore, 2020.
III. Aina, F., Yousef, S., & Osanaiye, O. ” Analysing admission control for AODV and DSR routing protocol in mobile ad-hoc network” . Bulletin of Electrical Engineering and Informatics, Vol.10, No. 5, pp.2667-2677, 2021.‏
IV. Al Mojamed, M., & Kolberg, M. ” Structured Peer-to-Peer overlay deployment on MANET: A survey” . Computer Networks, Vol. 96, pp. 29-47, 2016.
V. Barbudhe, S. E., Thakare, V. M., & Sherekar, S. S. ” A Novel Approach to Minimize Flooding in MANET Using Symmetric and Asymmetric Paths “. International Journal of Electronics, Communication and Soft Computing Science & Engineering (IJECSCSE), Vol. 3, No.18‏ , 2014.
VI. Chitkara, M., & Ahmad, M. W.” Review on manet: characteristics, challenges, imperatives and routing protocols “. International journal of computer science and mobile computing, Vol. 3, No. 2, pp. 432-437., 2014. ‏
VII. Gupta, N. K., Yadav, R. S., & Nagaria, R. K. (2020). ” 3D geographical routing protocols in wireless ad hoc and sensor networks: An overview” Wireless Networks, Vol. 26, No. 4, pp.2549-256, 2020.
VIII. Hamdi, M. M., Audah, L., Rashid, S. A., Mohammed, A. H., Alani, S., & Mustafa, A. S. ” A review of applications, characteristics and challenges in vehicular ad hoc networks (VANETs)” . In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-7). IEEE., 2020.
IX. Hassan, M. H., Mostafa, S. A., Mohammed, M. A., Ibrahim, D. A., Khalaf, B. A., & Al-Khaleefa, A. S. ” Integrating African Buffalo optimization algorithm in AODV routing protocol for improving the QoS of MANET “. Journal of Southwest Jiaotong University, Vol. 54, No. 3, 2019.‏
X. Lavanya, P., Reddy, V. S. K., & Prasad, A. M. ” Performance comparison of DSDV, OLSR, AODV and DSR for mobile ad hoc networks” . International Journal of Emerging Technology and Advanced Engineering, Vol. 8, No. 1, pp. 209-218, 2018.
XI. Mezaal, Yaqeen S., et al. “Cloud computing investigation for cloud computer networks using cloudanalyst.” Journal of Theoretical and Applied Information Technology, 96(20), 2018.
XII. Padmavathy, N. ” Reliability Evaluation of Environmentally Affected Mobile Ad Hoc Wireless Networks”. Advances in Communication and Computational Technology, pp. 1297-1310, 2021.
XIII. Padmavathy, N. ” Reliability Evaluation of Environmentally Affected Mobile Ad Hoc Wireless Networks” . Advances in Communication and Computational Technology, pp. 1297-1310.‏
XIV. Quy, V. K., Nam, V. H., Linh, D. M., Ban, N. T., & Han, N. D. ” A survey of QoS-aware routing protocols for the MANET-WSN convergence scenarios in IoT networks” . Wireless Personal Communications, Vol. 120, No. 1, pp. 49-62, 2021. ‏
XV. Roshani, Saeed, et al. “Filtering power divider design using resonant LC branches for 5G low-band applications.” Sustainability 14.19 (2022): 12291.
XVI. Saini, T. K., & Sharma, S. C. ” Prominent unicast routing protocols for Mobile Ad hoc Networks: Criterion, classification, and key attributes” . Ad Hoc Networks, Vol. 89, pp. 58-77.‏, 2019.
XVII. Shareef, M. S., Mezaal, Y. S., Sultan, N. H., Khaleel, S. K., Al-Hillal, A. A., Saleh, H. M., … & Al-Majdi, K. (2023). Cloud of Things and fog computing in Iraq: Potential applications and sustainability. Heritage and Sustainable Development, 5(2), 339-350.
XVIII. Shantaf, A. M., Kurnaz, S., & Mohammed, A. H.” Performance evaluation of three mobile ad-hoc network routing protocols in different environments” . In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1-6). IEEE, 2020.
XIX. Sharma, V., Alam, B., & Doja, M. N. ” An improvement in DSR routing protocol of MANETs using ANFIS ” . In Applications of Artificial Intelligence Techniques in Engineering (pp. 569-576). Springer, Singapore, 2019. ‏
XX. Souidi, M., Habbani, A., Berradi, H., & El Mahdi, F. ” Geographic forwarding rules to reduce broadcast redundancy in mobile ad hoc wireless networks” . Personal and Ubiquitous Computing, Vol. 23, No. (5), pp. 765-775, 2019‏.
XXI. Soundarya, D., Janane, S., & Ramachandran, K. A. ” A Survey On DSR Routing Protocol. International Journal of Research in Engineering, Science and Management, Vol. 4, No. 2, pp.142-145, 2021.
‏
XXII. Sureshbhai, T. H., Mahajan, M., & Rai, M. K. ” An investigational analysis of DSDV, AODV and DSR routing protocols in mobile Ad Hoc networks” . In 2018 International Conference on Intelligent Circuits and Systems (ICICS) (pp. 281-285). IEEE, 2018.
XXIII. Tahboush, M., & Agoyi, M. ” A hybrid wormhole attack detection in mobile ad-hoc network (MANET)”. IEEE Access, Vol. 9, pp. 11872-11883, 2021.
XXIV. Tahir, A., Shah, N., Abid, S. A., Khan, W. Z., Bashir, A. K., & Zikria, Y. B. ” A three-dimensional clustered peer-to-peer overlay protocol for mobile ad hoc networks “. Computers & Electrical Engineering, Vol. 94, pp. 107364, 2021‏.
XXV. Toh, C. K. ” Wireless ATM and ad-hoc networks: Protocols and architectures” . Springer Science & Business Media, 2012.‏
XXVI. Toh, C. K. ” Associativity-based routing for ad hoc mobile networks”. Wireless personal communications, Vol. 4, No. 2, pp.103-139, 1997‏.
XXVII. Yang, W., Zhang, H., & Lin, J. ” Simple applications of BERT for ad hoc document retrieval” . arXiv preprint arXiv:1903.10972, 2019.
XXVIII. Yaqeen S. Mezaal, Halil T. Eyyuboglu, and Jawad K. Ali. “New dual band dual-mode microstrip patch bandpass filter designs based on Sierpinski fractal geometry.” 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT). IEEE, 2013.
XXIX. Yaqeen S. Mezaal, Halil T. Eyyuboglu, and Jawad K. Ali. “A novel design of two loosely coupled bandpass filters based on Hilbert-zz resonator with higher harmonic suppression.” 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT). IEEE, 2013.
XXX. Yaqeen S. Mezaal, & Abdulkareem, S. F. (2018, May). New microstrip antenna based on quasi-fractal geometry for recent wireless systems. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
XXXI. Zanjireh, M. M., Shahrabi, A., & Larijani, H. ” Anch: A new clustering algorithm for wireless sensor networks “. In 2013 27th International Conference on Advanced Information Networking and Applications Workshops (pp. 450-455). IEEE, 2013.
XXXII. Zhang, J., Chen, T., Zhong, S., Wang, J., Zhang, W., Zuo, X., … & Hanzo, L. ” Aeronautical $ Ad~ Hoc $ networking for the Internet-above-the-clouds” . Proceedings of the IEEE, Vol. 107, No. 5, pp. 868-911, 2019.‏

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Abstract:

Advancements in multimodal biometrics, which amalgamate multiple biometric traits, hold promise for augmenting the accuracy and robustness of biometric identification systems. The focal point of this innovative study is the enhancement of multimodal biometrics identification, using face and iris images as the key biometric traits. This work taps into the expansive collection of face and iris images present in the WVU-Multimodal dataset for evaluation purposes. Our proposed approach employs “Convolutional Neural Network (CNN)” architectures, notable for their efficacy in computer vision tasks, to extract potent discriminative features from the input images. This work specifically incorporates three popular CNN architectures: ResNet-50, InceptionNet, XceptionNet, and fine-tuned CNN. To amalgamate the extracted features, investigate various fusion techniques in the security-centric industry: early fusion, and score-level fusion. Early fusion is an approach that merges the raw images of both face and iris at the input level to a single CNN model. Use the Gabor approach to enhance the image's quality and make the face and iris information more visible. This technique modifies the histogram equalization process for local regions, thus enabling better visibility and subsequent feature extraction. Our experimental evaluation employs performance metrics like accuracy, “Equal Error Rate”, and “Receiver Operating Characteristic” curves. In this work undertakes a comparative analysis to appraise the performance of the different CNN architectures and fusion techniques under scrutiny.

Keywords:

CNN Models,Deep Learning,Gabor Technique,Security,Fusion,

Refference:

I. A. Gutub, N. Al-Juaid, E. Khan: ‘Counting-based secret sharing technique for multimedia applications.’ Multimedia Tools and Applications 78 (2019): 5591-5619.
II. C. Kamlaskar, A. Abhyankar: ‘Multimodal System Framework for Feature Level Fusion based on CCA with SVM Classifier.’ 2020 IEEE-HYDCON. IEEE, 2020.
III. D. Singh, V. Kumar: ‘A comprehensive review of computational dehazing techniques.’ Archives of Computational Methods in Engineering 26.5 (2019): 1395-1413.
IV. F. Cherifi, K. Amroun, M. Omar: ‘Robust multimodal biometric authentication on IoT device through ear shape and arm gesture.’ Multimedia Tools and Applications 80.10 (2021): 14807-14827.
V. F. Wang, J. Han: ‘Robust multimodal biometric authentication integrating iris, face and palmprint.’ Information technology and control 37.4 (2008).
VI. I. Boucherit, M.O. Zmirli, H. Hentabli and B.A. Rosdi: ‘Finger vein identification using deeply-fused Convolutional Neural Network.’ Journal of King Saud University-Computer and Information Sciences 34.3 (2022): 646-656.
VII. J. Lowe: ‘Ocular Motion Classification for Mobile Device Presentation Attack Detection.’ University of Missouri-Kansas City, 2020.
VIII. K. Gunasekaran, J. Raja, R. Pitchai: ‘Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images.’ Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije 60.3 (2019): 253-265.
IX. K.P. Kumar, P.K. Prasad, Y. Suresh, M.R. Babu, M.J. Kumar: ‘Ensemble recognition model with optimal training for multimodal biometric authentication.’ Multimedia Tools and Applications (2024): 1-25.
X. L. Wan, K. Liu, H.A. Mengash., N. Alruwais, M. Al Duhayyim, K. Venkatachalam, : ‘Deep learning-based photoplethysmography biometric authentication for continuous user verification.’ Applied Soft Computing 156 (2024): 111461Darren Williams. : ‘Concrete Strength Prediction from Early-Age Data’. Technical Paper, Honor Project, Technical Paper, University of Adelaide.
XI. L. Wang, X. Meng, D. Li, X. Zhang, S. Ji, S. Guo: ‘DEEPFAKER: a unified evaluation platform for facial deepfake and detection models.’ ACM Transactions on Privacy and Security, 27(1), pp.1-34, 2024.
XII. P. Dhiman, V. Kukreja, A. Kaur: ‘Citrus fruits classification and evaluation using deep convolution neural networks: an input layer resizing approach.’ 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO). IEEE, 2021.
XIII. P. Sivakumar, B.R. Rathnam, S. Divakar, M.A. Teja, R.R. Prasad: ‘A Secure and Compact Multimodal Biometric Authentication Scheme using Deep Hashing.’ 2021 IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT). IEEE, 2021.
XIV. P. Xiao: ‘Network Malware Detection Using Deep Learning Network Analysis.’ Journal of Cyber Security and Mobility, pp.27-52, 2024.
XV. P.P. Sarangi, D.R. Nayak, M. Panda, B. Majhi : ‘A feature-level fusion based improved multimodal biometric recognition system using ear and profile face.’ Journal of Ambient Intelligence and Humanized Computing 13.4 (2022): 1867-1898.
XVI. P.P. Sarangi, D.R. Nayak, M. Panda, B. Majhi: ‘A feature-level fusion based improved multimodal biometric recognition system using ear and profile face.’ Journal of Ambient Intelligence and Humanized Computing 13.4 (2022): 1867-1898.
XVII. P.S. Chanukya, T. K. Thivakaran.: ‘Multimodal biometric cryptosystem for human authentication using fingerprint and ear.’ Multimedia Tools and Applications 79.1 (2020): 659-673.
XVIII. Q. Jiang, G. Zhao, X. Ma, M. Li, Y. Tian, X. Li, : ‘Cross-modal Learning based Flexible Bimodal Biometric Authentication with Template Protection.’ IEEE Transactions on Information Forensics and Security (2024).
XIX. R. Deshmukh,P. Yannawar: ‘Deep learning based person authentication system using fingerprint and brain wave.’ International Journal of Computing and Digital Systems 15.1 (2024): 723-739.
XX. R. Ryu, S. Yeom, S.H. Kim, D. Herbert.: ‘Continuous multimodal biometric authentication schemes: a systematic review.’ IEEE Access 9 (2021): 34541-34557.
XXI. R.A. Ramirez-Mendoza, J.D. Lozoya-Santos, R. Zavala-Yoé, L.M. Alonso-Valerdi, R. Morales-Menendez, B. Carrión, P.P. Cruz, H.G. Gonzalez-Hernandez: ‘Biometry: Technology, Trends and Applications.’ CRC Press; 2022 Jul 7.
XXII. R.O. Mahmoud, M. M. Selim, O.A. Muhi: ‘Fusion time reduction of a feature level based multimodal biometric authentication system.’ International Journal of Sociotechnology and Knowledge Development (IJSKD) 12.1 (2020): 67-83.
XXIII. S. Aleem, P. Yang, S. Masood, P. Li, B. Sheng: ‘An accurate multi-modal biometric identification system for person identification via fusion of face and finger print.’ World Wide Web 23.2 (2020): 1299-1317.
XXIV. S. Balaji, U. Rahamathunnisa: ‘Multimodal Biometrics Authentication in Healthcare Using Improved Convolution Deep Learning Model.’ INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 32.03 (2023): 2340013.
XXV. S. Pahuja, N. Goel: ‘State-of-the-Art Multi-trait Based Biometric Systems: Advantages and Drawbacks.’ International Conference on Emerging Technologies in Computer Engineering. Cham: Springer International Publishing, 2022.
XXVI. S. Salturk, N. Kahraman: ‘Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security.’ Neural Computing and Applications (2024): 1-12.
XXVII. T. Gernot, C. Rosenberger: ‘Robust biometric scheme against replay attacks using one-time biometric templates.’ Computers & Security 137 (2024): 103586.
XXVIII. U. Sumalatha, K.K. Prakasha, S. Prabhu, V.C. Nayak: ‘A Comprehensive Review of Unimodal and Multimodal Fingerprint Biometric Authentication Systems: Fusion, Attacks, and Template Protection.’ IEEE Access (2024).
XXIX. V. Talreja, C. V. Matthew, M.N. Nasser: ‘Multibiometric secure system based on deep learning.’ 2017 IEEE Global conference on signal and information processing
XXX. WVU Multimodal Dataset. Accessed: Jan. 28, 2018. [Online]. Available: http://biic.wvu.edu/
XXXI. X. Zhang, D. Cheng, P. Jia, Y. Dai, X. Xu: ‘An efficient android-based multimodal biometric authentication system with face and voice.’ IEEE Access 8 (2020): 102757-102772.
XXXII. X. Zhang, L. Yao, C. Huang, T. Gu, Z. Yang, Y. Liu: ‘DeepKey: A multimodal biometric authentication system via deep decoding gaits and brainwaves.’ ACM Transactions on Intelligent Systems and Technology (TIST) 11.4 (2020): 1-24.
XXXIII. Y. Xu, A. Zhong, J. Yang, D. Zhang: ‘Bimodal biometrics based on a representation and recognition approach.’ Optical Engineering 50.3 (2011): 037202-037202.
XXXIV. Y. Yin, S. He, R. Zhang, H. Chang, X. Han, J. Zhang: ‘Deep learning for iris recognition: a review.’ arXiv preprint arXiv:2303.08514 (2023).
XXXV. Z. Boulkenafet, J. Komulainen, A. Hadid: ‘Face spoofing detection using colour texture analysis.’ IEEE Transactions on Information Forensics and Security 11.8 (2016): 1818-1830.

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Abstract:

The increase of attacks on bank accounts and credit cards through various types of attacks. Also, the rapid growth of online bank transfers and rapid transactions in stores and marketing worldwide make the urgent to use ciphering for securing the transactions process. In this paper, ways are proposed to enhance the algorithm that used ciphering and authentications of the user to ensure that the same user made the transactions. This is done through using the blockchain such as the Hyperledger fabric with the using the Internet of things for the authentications. The proposed algorithm helps in improving the safety of online transactions and helps protect the information of the user through the several nodes that use IOT for verifications and authentications. The results enhanced the blockchain of the Hyperledger fabric by enhancing the ways of the transactions and the process through the power of IoT.

Keywords:

Complex Network,Hyperledge Fabric,IoT,Online Transactions,

Refference:

I. AbdulAmeer, Sabah Auda, et al. “Cyber Security Readiness in Iraq: Role of the Human Rights Activists.” International Journal of Cyber Criminology 16.2 (2022): 1-14.
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IV. E. Androulaki, A. Barger, V. Bortnikov, C. Cachin, K. Christidis, A. D. Caro, D. Enyeart, C. Ferris, G. Laventman, Y. Manevich, S. Muralidharan, C. Murthy, B. Nguyen, M. Sethi, G. Singh, K. Smith, A. Sorniotti, C. Stathakopoulou, M. Vukolic, S. W. Cacco and J. Yellick, “Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains,” In Proc. 2018 the Thirteenth EuroSys Conference (EuroSys’18), No. 30, Porto, Portugal, April 23-26, 2018.
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VI. H. Sukhwani, N. Wang, K. S. Trivedi and A. Rindos, “Performance Modeling of Hyperledger Fabric (Permissioned Blockchain Network),” In Proc. 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, 2018, pp. 1-8.
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VIII. N. Reiff, Blockchain Technology’s Three Generations, 2020. [Online]. Available: https://www.investopedia.com/tech/blockchain-technologys-three-generations/
IX. P. Thakkar, S. Nathan and B. Viswanathan, “Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform,” 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Milwaukee, WI, 2018, pp. 264-276.
X. Q. Nasir, I. A. Qasse, M. A. Talib, and A. B. Nassif, “Performance Analysis of Hyperledger Fabric Platforms,” Security and Communication Networks, vol. 2018, Article ID 3976093, 14 pages, 2018. 10.1155/2018/3976093.
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XII. S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” [Online]. Available: http://www.bitcoin.org. Retrieved: April 2019.
XIII. S. Pongnumkul, C. Siripanpornchana and S. Thajchayapong, “Performance Analysis of Private Blockchain Platforms in Varying Workloads,” In Proc. 2017 the 26th International Conference on Computer Communication and Networks (ICCCN), Vancouver, BC, Canada, 2017, pp. 1-6.
XIV. T. T. Dinh, J. Wang, G. Chen, R. Liu, B. C. Ooi, and K. Tan, “Blockbench: A framework for analyzing private blockchains,” in Proc. the 2017 ACM International Conference, pp. 1085–1100, Chicago, IL, USA, May 14-19, 2017.
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XVI. Yaqeen S. Mezaal, Halil T. Eyyuboglu, and Jawad K. Ali. “New dual band dual-mode microstrip patch bandpass filter designs based on Sierpinski fractal geometry.” 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT). IEEE, 2013.
XVII. Yaqeen S. Mezaal, Halil T. Eyyuboglu, and Jawad K. Ali. “A novel design of two loosely coupled bandpass filters based on Hilbert-zz resonator with higher harmonic suppression.” 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT). IEEE, 2013.
XVIII. Yaqeen S. Mezaal, & Abdulkareem, S. F. (2018, May). New microstrip antenna based on quasi-fractal geometry for recent wireless systems. In 2018 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
XIX. Yaqeen S. Mezaal, et al. “Cloud computing investigation for cloud computer networks using cloudanalyst.” Journal of Theoretical and Applied Information Technology, 96(20), 2018.
XX. Yaqeen S. Mezaal, et al. Cloud of Things and fog computing in Iraq: Potential applications and sustainability. Heritage and Sustainable Development, 5(2), 339-350.
XXI. Yaqeen S. Mezaal, et al.”Filtering power divider design using resonant LC branches for 5G low-band applications.” Sustainability 14.19 (2022): 12291.

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Abstract:

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using three types of Monte Carlo simulation techniques. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing the Monte Carlo technique to extract maximum energy profit using the hybrid microgrid. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.

Keywords:

Battery Storage,Energy Management System,Microgrids,Monte Carlo Optimization,Optimization,Photovoltaic (PV),Uncertainties,Wind Energy,

Refference:

I. Alonso, O., Sanchis, P., Gubia, E., & Marroyo, L. (2003). Cascaded H-bridge multilevel converter for grid-connected photovoltaic generators with independent maximum power point tracking of each solar array. Proceedings of the 34th IEEE Power Electronics Specialists Conference, Acapulco, Mexico, 15–19 June 2003, 2, 731–735. New York: IEEE.
II. Alvarez, G., Moradi, H., Smith, M., & Zilouchian, A. (2017). Modeling a grid-connected PV/Battery microgrid system with MPPT controller. 2017 IEEE 44th Photovoltaic Specialist Conference (PVSC), 2941–2946.
III. Almada, J., Leão, R., Sampaio, R., & Barroso, G. (2016). A centralized and heuristic approach for energy management of an AC microgrid. Renewable and Sustainable Energy Reviews, 60, 1396–1404.
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V. Arcos-Aviles, D., Pascual, J., Guinjoan, F., Marroyo, L., Sanchis, P., & Marietta, M. P. (2017). Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting. Applied Energy, 205, 69–84.
VI. Cabrera-Tobar, A., Massi Pavan, A., Petrone, G., & Spagnuolo, G. (2022). A review of the optimization and control techniques in the presence of uncertainties for the energy management of microgrids. Energies, 15(23), 9114. https://doi.org/10.3390/en15239114.
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Abstract:

We consider, in this paper, the NP-hard problem of finding the minimum independent domination metric dimension of graphs. A vertex set  of a connected graph  resolves  if every vertex of  is uniquely identified by its vector of distances to the vertices in . A resolving set  of  is independent if no two vertices in  are adjacent. A resolving set is dominating if every vertex of  that does not belong to  is a neighbor to some vertices in . The cardinality of the smallest resolving set of , the cardinality of the minimal independent resolving set, and the cardinality of the minimal independent domination resolving set are the metric dimension of , independent metric dimension of , and the independent domination metric dimension of , respectively.

Keywords:

Dominant Metric Dimension,Domination Number,Independent Number,Metric Dimension,Resolving Dominating Set,

Refference:

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