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Low-power wide-area network (LPWAN) has emerged as a dominating communication technology that offers low-power and wide coverage for the Internet of Things (IoT) applications. However, the direct data transmission approach has a limited network lifetime. Even multihop data transmission experiences many difficulties including high data latency, poor bandwidth utilization, and reduced data throughput. To overcome these challenges, in this article, a recent breakthrough in social networks known as small-world characteristics (SWC) is incorporated into LPWANs. In particular, in this work, small-world LPWANs (SW-LPWANs) are developed by using the reinforcement learning (RL) technique and using different node centrality measures like degree, betweenness, and closeness centrality. Furthermore, the performance of the developed SW-LPWANs is evaluated in terms of energy efficiency (alive/dead devices, and network residual energy) and quality-of-service (QoS) (average data latency, data throughput, and bandwidth utilization) and is compared with that of conventional multihop LPWAN. Finally, to validate the simulation results, similar analyses are performed on the real-field LPWAN testbed. The obtained simulation results confirm that an SW-LPWAN developed by the RL method performs better than other techniques, with ${11}\%$ more alive devices, ${5}.{5}\%$ higher residual energy, ${2}.{4}\%$ improved data throughput, and ${14}\%$ efficient bandwidth utilization compared to the next best method. A similar trend is observed with real-field LPWAN testbed data also.