Author(s): HN Kama, Thomas Kalu and Oghenetega Avwokuruaye

Abstract: This paper develops an operations research framework for energy-efficient encoding in IoT networks. The encoding problem is modeled as an optimization task, where the objective is to minimize total energy cost comprising computational energy for encoding and transmission energy proportional to code length. Decision variables include encoding scheme selection and codeword assignment, subject to Kraft’s inequality, latency, and memory constraints. A goal programming approach is used to balance energy efficiency and reliability. The model provides a mathematical foundation linking information theory and optimization, offering new insights into sustainable IoT data transmission. Arithmetic coding is closest to the entropy bound 100% efficient, Huffman coding achieves good efficiency 85-90% and Fixed-length encoding is least efficient 82%. Fixed-length encoding has the lowest total energy (3.5 units), since computation cost is minimal even though more bits are transmitted. Huffman coding requires more computation, raising its total energy slightly (3.9 units). Arithmetic coding saves transmission energy but has the highest computational cost, giving it the largest total energy (3.97 units).

DOI: 10.33545/2707661X.2025.v6.i2b.150

Pages: 138-142 | Views: 99 | Downloads: 65

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