Abstract : Massive numbers of sensor nodes are used in wireless sensor networks (WSNs) to gather data about the immediate environment, but this data is meaningless unless the precise location from which it was gathered is made known. In many applications, including spotting enemy movement in military applications, localization of sensor nodes in WSNs is significant. Finding the coordinates of all target nodes with the aid of anchor nodes is the main goal of the localization issue. Two variations of the zebra optimization algorithm (ZOA) are suggested in this study to localize the sensor nodes more effectively and to get beyond the original ZOA's limitations, such as becoming stuck in local optimal solutions. The suggested ZOA versions 1 and 2 modify the exploration and exploitation aspects of the original ZOA by utilizing better global and local search algorithms. Numerous simulations have been run with varying numbers of target nodes and anchor nodes to test the efficacy of the proposed ZOA versions 1 and 2, and the results are compared with those of the original ZOA and other known optimization methods used to solve the node localization problem. When it comes to mean localization error, the quantity of localized nodes, and computation time, the suggested ZOA versions 1 and 2 perform better than the competition. Additionally, given a range of target and anchor node values, the suggested ZOA variants 1 and 2 and the original ZOA are evaluated in terms of different mistakes and localization efficacy. The simulation results show that the proposed ZOA variation 2 has a number of advantages over the proposed ZOA variant 1 and the current ZOA. In comparison to the proposed ZOA variation 1, ZOA, and other current optimization methods, the node localization based on the suggested ZOA variant 2 is more efficient since calculations are completed faster and mean localization error is lower.
Keywords : Node localization, Bat optimization algorithm, Computation time, Target nodes, Localization error.
Cite :
References :
Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7-74.
Brookhart, S. M. (2013). How to create and use rubrics for formative assessment and grading. ASCD.
Brown, G. T. L. (2004). Teachers' conceptions of assessment: Implications for policy and professional development. Assessment in Education: Principles, Policy & Practice, 11(3), 301-318.
Darling-Hammond, L., & Adamson, F. (2014). Beyond the bubble test: How performance assessments support 21st-century learning. Jossey-Bass.
Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.
Gardner, H. (1993). Multiple intelligences: The theory in practice. Basic Books.
Gipps, C. V. (1994). Beyond testing: Towards a theory of educational assessment. Falmer Press.
Government of India. (2020). National Education Policy 2020. Ministry of Human Resource Development.
Harlen, W. (2007). The quality of learning: Assessment alternatives for primary education. Primary Science Review, 101, 27-29.
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81-112.
Heritage, M. (2010). Formative assessment: Making it happen in the classroom. Corwin Press.
Jain, S., & Prasad, K. (2021). A shift from traditional assessment to holistic progress reports: A key feature of NEP 2020. Journal of Educational Innovation, 10(2), 89-97.
Ministry of Education. (2020). National Education Policy 2020. Government of India.
NCERT. (2020). Guidelines for holistic assessment. National Council of Educational Research and Training.
Popham, W. J. (2001). The truth about testing: An educator’s call to action. ASCD.
Tomlinson, C. A. (2005). Grading and differentiation: Paradox or good practice? Theory into Practice, 44(3), 262-269.
