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A Node Location Algorithm Based on Improved Grasshopper Optimization in Wireless Sensor Network


Affiliations
1 Department of Computer Science and Application, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya University, India
     

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As the use of swarm intelligence algorithms grows, so does the interest in placing nodes in a Wireless Sensor network. For this reason, the RSSI range model positioning algorithm has been replaced by a more accurate one. With the help of this paper, you can solve complex structural optimization problems with the Grasshopper Optimization Algorithm (GOA). Optimization problems can be solved using this algorithm, which was inspired by the behaviour of grasshopper colonies. CEC2005 is used to test the GOA algorithm quality and quantitative performance. Trusses with a total of 53 and 3 cantilever beams are used to demonstrate the design practicality. It appears that the proposed algorithm outperforms well-known and recently developed algorithms in this area. GOA ability to solve real-world problems with unknown search spaces is demonstrated by its use in the real world.

Keywords

WSN (Wireless Sensor Network), GOA (Grasshopper Optimization Algorithm), RSSI, Whale Optimization Algorithm
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  • P.Sindhuja and P.Ramamoorthy, “An Improved Fuzzy enabled Optimal Multipath routing for Wireless Sensor Networks”, Cluster Computing, Vol. 78, No. 2, pp. 1-15, 2017.

  • M. Ghalambaz, R.J. Yengejeh and A.H. Davami, “Building Energy Optimization using Grey Wolf Optimizer (GWO)”, Case Studies in Thermal Engineering, Vol. 27, pp. 1-13, 2021.

  • Z.Y. Algamal and H.T. Ali, “Improving Grasshopper Optimization Algorithm for Hyperparameters Estimation and Feature Selection in Support Vector Regression”, Chemometrics and Intelligent Laboratory Systems, Vol. 208, pp. 104196-104201, 2021.

  • B.S. Yıldız, A.R. Yıldız, E.S. Albak, H. Abderazek, S.M. Sait and S. Bureerat, “Butterfly Optimization Algorithm for Optimum Shape Design of Automobile Suspension Components”, Materials Testing, Vol. 62, No. 4, pp. 365-370, 2020.

  • B.S. Yildiz, N. Pholdee, S. Bureerat, A.R. Yildiz and S.M. Sait, “Robust Design of a Robot Gripper Mechanism using New Hybrid Grasshopper Optimization Algorithm”, Expert Systems, Vol. 38, No. 3, pp. 1-16, 2021.

  • A.R. Yıldız and M.U. Erdaş, “A New Hybrid Taguchi-Salp Swarm Optimization Algorithm for the Robust Design of Realworld Engineering Problems”, Materials Testing, Vol. 63, No. 2, pp. 157-162, 2021.

  • N. Panagant, N. Pholdee, S. Bureerat, K. Kaen, A.R. Yıldız and S.M. Sait, “Seagull Optimization Algorithm for Solving Realworld Design Optimization Problems”, Materials Testing, Vol. 62, No. 6, pp. 640-644, 2020.

  • A.R. Yıldız, B.S. Yıldız and S.M. Sait, “The Equilibrium Optimization Algorithm and the Response Surface-Based Metamodel for Optimal Structural Design of Vehicle Components”, Materials Testing, Vol. 62, No. 5, pp. 492-496, 2020.

  • H. Abderazek, S.M. Sait and A.R. Yildiz, “Optimal Design of Planetary Gear Train for Automotive Transmissions using Advanced Meta-Heuristics”, International Journal of Vehicle Design, Vol. 80, No. 2-3, pp. 121-136, 2019.

  • L. Chen, L. Pang and B. Zhou, “RLAN: Range-Free Localisation based on Anisotropy of Nodes for WLANs”, Electronics Letters, Vol. 51, No. 24, pp. 2066-2068, 2015.

  • L. Sun, Y. Yuan, Q. Xu, C. Hua and X. Guan, “A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks”, Sensors, Vol. 19, No. 20, pp. 4369-4389, 2019.

  • S.P. Maruthi and T. Panigrahi, “Robust Mixed Source Localization in WLAN using Swarm Intelligence Algorithms”, Digital Signal Processing, Vol. 98, No. 98, pp. 102651-102658, 2020.

  • V. Bianchi, P. Ciampolini and I. De Munari, “RSSI-based Indoor Localization and Identification for ZigBee Wireless Sensor Networks in Smart Homes”, IEEE Transactions on Instrumentation and Measurement, Vol. 68, No. 2, pp. 566-575, 2019.

  • C. Muller, D.I. Alves and J.B.S. Martins, “Improved Solution for Node Location Multilateration Algorithms in Wireless Sensor Networks”, Electronics Letters, Vol. 52, No. 13, pp. 1179-1181, 2016.

  • S. Pan, S. Hua, D.W. Pan and X. Sun, “Wireless Localization Method based on AHP-WKNN and Amendatory AKF”, Wireless Communications and Mobile Computing, Vol. 2021, pp. 1-11, 2021.

  • H. Li, D. Yu, Y. Hu and H.Y. Yu, “Improved Trilateral Centroid Localization Algorithm for Wireless Sensor Networks”, Journal of Chinese Computer Systems, Vol. 41, No. 6, pp. 1216-1223, 2020.

  • H. Ahmadi, F. Viani and R. Bouallegue, “An Accurate Prediction Method for Moving Target Localization and Tracking in Wireless Sensor Networks”, Ad Hoc Networks, Vol. 70, pp. 14-22, 2018.

  • S. Shah, C. Zhe and F.L. Yin, “3D Weighted Centroid Algorithm and RSSI Ranging Model Strategy for Node Localization in WLAN based on Smart Devices”, Sustainable Cities and Society, Vol. 39, pp. 298-308, 2018.

  • K. Ren and C.M. Pan, “A Novel DV-Hop Algorithm for RSSI Hop Quantization and Error Correction”, Chinese Journal of Sensors and Actuators, Vol. 33, No. 5, pp. 718-724, 2020.

  • L.M. Schmitt and M. Schmitt, “Theory of Genetic Algorithms”, Theoretical Computer Science, Vol. 259, No. 1-2, pp. 1-61, 2001.

  • M. Dorigo, M. Birattari and T. Stutzle, “Ant Colony Optimization”, IEEE Computational Intelligence Magazine, Vol. 1, No. 4, pp. 28-39, 2009.

  • X.S. Yang, “Firefly Algorithms for Multimodal Optimization”, Mathematics, vol. 5792, pp. 169-178, 2009.

  • S. Mirjalili, M. Mirjalili and A. Lewis, “Grey Wolf Optimizer”, Advances in Engineering Software, Vol. 69, pp. 46-61, 2014.

  • S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm”, Advances in Engineering Software, Vol. 95, No. 5, pp. 51-67, 2016.

  • S.M. Li, H.L. Chen, M.J. Wang, A.A. Heidari and S. Mirjalili, “Slime Mould Algorithm: A New Method for Stochastic Optimization”, Future Generation Computer Systems, Vol. 111, pp. 300-323, 2020.

  • Y.T. Yang, H.L. Chen, A.A. Heidari and A.H. Gandomi, “Hunger Games Search: Visions, Conception, Implementation, Deep Analysis, Perspectives, and Towards Performance Shifts”, Expert Systems with Applications, Vol. 177, pp. 114864-114875, 2021.

  • A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja and H. Chen, “Harris Hawks Optimization: Algorithm and Applications”, Future Generation Computer Systems, Vol. 97, pp. 849-872, 2019.

  • S. Phoemphon and N. Leelathakul, “Improved Distance Estimation with Node Selection Localization and Particle Swarm Optimization for Obstacle-Aware Wireless Sensor Networks”, Expert Systems with Applications, Vol. 175, pp. 114773-114785, 2021.

  • F.S. Gharehchopogh and H. Gholizadeh, “A Comprehensive Survey: Whale Optimization Algorithm and its Applications”, Swarm Evolutionary Computing, Vol. 48, pp. 1-24, 2019.

  • Y. Meng, Q. Zhi, Q. Zhang and E. Lin, “A Two-Stage Wireless Sensor Grey Wolf Optimization Node Location Algorithm based on K-Value Collinearity”, Mathematical Problems in Engineering, Vol. 2020, pp. 1-10, 2020.

  • H.M. Kanoosh, E.H. Houssein and M.M. Selim, “Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks”, Journal of Computer Networks and Communications, Vol. 2019, pp. 1-12, 2019.

  • J.S. Pan, F. Fan, S. Chu, Z. Du and H. Zhao, “A Node Location Method in Wireless Sensor Networks based on a Hybrid Optimization Algorithm”, Wireless Communications and Mobile Computing, Vol. 2020, pp. 1-14, 2020.

  • J. Luo, H. Chen, A.A. Heidari, Y. Xu, Q. Zhang and C. Li, “Multi-Strategy Boosted Mutative Whale-Inspired Optimization Approaches”, Applied Mathematical Modelling, Vol. 73, pp. 109-123, 2019.

  • Ahmed A Ewees, Mohamed Abd Elaziz and Essam H Houssein, “Improved Grasshopper Optimization Algorithm using Opposition-Based Learning”, Expert Systems with Applications, Vol. 112, pp. 156-172, 2018.

  • Laith Abualigah and Ali Diabat, “A Comprehensive Survey of the Grasshopper Optimization Algorithm: Results, Variants, and Applications”, Neural Computing and Applications, Vol. 78, pp. 1-24, 2020.

  • Shahrzad Saremi, Seyedali Mirjalili and Andrew Lewis, “Grasshopper Optimisation Algorithm: Theory and Application”, Advances in Engineering Software, Vol. 105, pp. 30-47, 2017.

  • S. Saremi, S. Mirjalili and A. Lewis, “Grasshopper Optimisation Algorithm: Theory and Application”, Advances in Engineering Software, Vol. 105, pp. 30-47, 2017.

  • H. Pinto, A. Pena, M. Valenzuela and A. Fernandez, “A Binary Grasshopper Algorithm Applied to the Knapsack Problem”, Artificial Intelligence and Algorithms in Intelligent Systems, Vol. 764, pp. 1-14, 2019.

  • B. Crawford, R. Soto, A. Pena and G. Astorga, “A Binary Grasshopper Optimisation Algorithm applied to the Set Covering Problem”, Cybernetics and Algorithms in Intelligent Systems, Vol. 765, pp. 1-16, 2019.

  • H. Hichem, M. Elkamel, M. Rafik, M.T. Mesaaoud and C. Ouahiba, “A New Binary Grasshopper Optimization Algorithm for Feature Selection Problem”, Journal of King Saud University - Computer and Information Sciences, Vol. 34, pp. 1-17, 2019.

  • A. Saxena, S. Shekhawat and R. Kumar, “Application and Development of Enhanced Chaotic Grasshopper Optimization Algorithms”, Modelling and Simulation in Engineering, Vol. 2018, pp. 1-14, 2018.

  • J. Luo, H. Chen, Q. Zhang, Y. Xu, H. Huang and X. Zhao, “An Improved Grasshopper Optimization Algorithm with Application to Financial Stress Prediction”, Applied Mathematical Modelling, Vol. 64, pp. 654-668, 2018.

  • H. Zhang, Z. Gao, J. Zhang and G. Yang, “Visual Tracking with Levy Flight Grasshopper Optimization Algorithm”, Pattern Recognition and Computer Vision, Vol. 11857, pp. 1-17, 2019.

  • M. Mafarja, I. Aljarah, A.A. Heidari, A.I. Hammouri, H. Faris and, A.M. Al-Zoubi, “Evolutionary Population Dynamics and Grasshopper Optimization Approaches for Feature Selection Problems”, Knowledge-Based Systems, Vol. 145, pp. 25-45, 2018.

  • J. Wu, H. Wang, N. Li, P. Yao, Y. Huang and Z. Su, “Distributed Trajectory Optimization for Multiple Solar-Powered UAVs Target Tracking in Urban Environment by Adaptive Grasshopper Optimization Algorithm”, Aerospace Science and Technology, Vol. 70, pp. 497-510, 2017.

  • S.R. Gampa, K. Jasthi, P. Goli, D. Das and R.C. Bansal, “Grasshopper Optimization Algorithm based Two Stage Fuzzy Multiobjective Approach for Optimum Sizing and Placement of Distributed Generations Shunt Capacitors and Electric Vehicle Charging Stations”, Journal of Energy Storage, Vol. 27, pp. 1-16, 2020.

  • A.A. Ewees, M. Abd Elaziz and E.H. Houssein, “Improved Grasshopper Optimization Algorithm using Opposition-Based Learning”, Expert System and Applications, Vol. 112, pp. 156-172, 2018.

  • Z. Elmi and M.O. Efe, “Multi-Objective Grasshopper Optimization Algorithm for Robot Path Planning in Static Environments”, Proceedings of IEEE International Conference on Industrial Technology, pp. 244-249, 2018.

  • J. Liu, A. Wang, Y. Qu and W. Wang, “Coordinated Operation of Multi-Integrated Energy System based on Linear Weighted Sum and Grasshopper Optimization Algorithm”, IEEE Access, Vol. 6, pp. 42186-42195, 2018.

  • Q. Fan, X. Yan and Y. Xue, “Prior Knowledge Guided Differential Evolution”, Soft Computing, Vol. 21, No. 22, pp. 6841-6858, 2017.


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  • A Node Location Algorithm Based on Improved Grasshopper Optimization in Wireless Sensor Network

Abstract Views: 192  |  PDF Views: 1

Authors

D. Archana
Department of Computer Science and Application, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya University, India
S. Prakasam
Department of Computer Science and Application, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya University, India

Abstract


As the use of swarm intelligence algorithms grows, so does the interest in placing nodes in a Wireless Sensor network. For this reason, the RSSI range model positioning algorithm has been replaced by a more accurate one. With the help of this paper, you can solve complex structural optimization problems with the Grasshopper Optimization Algorithm (GOA). Optimization problems can be solved using this algorithm, which was inspired by the behaviour of grasshopper colonies. CEC2005 is used to test the GOA algorithm quality and quantitative performance. Trusses with a total of 53 and 3 cantilever beams are used to demonstrate the design practicality. It appears that the proposed algorithm outperforms well-known and recently developed algorithms in this area. GOA ability to solve real-world problems with unknown search spaces is demonstrated by its use in the real world.

Keywords


WSN (Wireless Sensor Network), GOA (Grasshopper Optimization Algorithm), RSSI, Whale Optimization Algorithm

References