The Internet of Things (IoT) demands connectivity solutions beyond traditional networks (WiFi, 3G/4G), driving the adoption of LPWAN (Low Power Wide Area Networks) for long-range, low-energy, and low-cost communication. Data compression directly reduces packet size without increasing receiver complexity or compromising reliability, optimizing bandwidth, energy efficiency, and transmission robustness. This work evaluates Huffman Coding and LZ77 compression on TTGO T-Beam v1.0 ESP32 LoRa devices in real-world IoT scenarios. Experimental results demonstrate that Huffman Coding consistently outperforms LZ77, achieving higher compression rates with lower computational overhead, thereby reducing transmission time and energy consumption. These findings highlight the efficacy of Huffman-based compression in LPWAN systems, while adaptive algorithm integration could further enhance IoT scalability and efficiency.