The exponential growth of data in IoT environments requires the development of resource-efficient feature selection methods adapted for constrained devices. This work introduces two novel efficiency modifications for the ReliefF method. The first enhancement involves fixed-point arithmetic, which lowers computational overhead while improving energy and memory efficiency. The second improvement involves a single-linkage clustering step that reduces sample size without compromising data relevance. Evaluated on nine diverse datasets, ranging from those with high sample numbers to microarrays, proposed optimizations demonstrate robust performance. A 16-bit fixed-point representation achieves feature rankings comparable to 64-bit floating-point baselines, offering significant efficiency improvements. The clustering step drastically reduces execution times for large-sample datasets while preserving classification accuracy. Key findings show that the integer part of fixed-point representation, which determines the representable range, is more critical than precision. Furthermore, 16-bit implementations provide an optimal balance for most IoT applications, and clustering is vital for scalability in datasets with numerous samples. These innovations make ReliefF an effective solution for energy-limited IoT systems.