The rapid expansion of Internet of Things (IoT) applications has driven advancements in networking technologies like Low-Power Wide-Area Networks (LPWANs) to extend coverage and enhance the lifespan of IoT devices (IoDs). However, real-world IoT networks are typically heterogeneous, comprising static and dynamic IoDs leading to variations in network topology. These fluctuations cause challenges like increased data latency and energy imbalances, which hinder efficient information flow. To overcome these issues, this paper presents a novel approach that integrates Small-World Characteristics (SWC), inspired by social network theory, into heterogeneous LPWANs using reinforcement learning. Specifically, the Q-learning technique is employed to introduce new long-range links into the network, enhancing connectivity and optimizing performance. Different conventional networks with varying numbers of mobile nodes are studied in this work followed by their subsequent transformation to small-world versions. The performance of the networks is optimized in terms of energy efficiency and latency in data routing. It is observed that, irrespective of the network (conventional or small-world), the performance is better if the number of static nodes is greater. Furthermore, independent of the degree of dynamicity, the SW-LPWAN is more energy efficient and has lower transmission delay than the corresponding conventional network. Numerically, SWLPWANs achieve up to 14.6% faster data transmission speeds, supporting 19.7% more active IoDs, and maintaining 15.5% higher residual energy compared to conventional networks.