The field of Internet of Things (IoT) is witnessing significant developments in resource allocation and management, driven by the increasing demand for efficient and adaptive systems. Researchers are exploring innovative approaches to optimize resource utilization, balance competing objectives, and adapt to dynamic environments. Notably, techniques such as multi-objective Q-learning, deep reinforcement learning, and graph neural networks are being applied to various IoT scenarios, including routing, simultaneous wireless information and power transfer (SWIPT), and mobile edge computing (MEC). These advances have the potential to significantly improve the performance and efficiency of IoT systems. Some noteworthy papers in this area include: Dynamic and Distributed Routing in IoT Networks based on Multi-Objective Q-Learning, which proposes a novel routing algorithm that adapts to changes in preferences in real-time. Cell-Free Massive MIMO-Assisted SWIPT for IoT Networks, which achieves EE performance gains of up to 4-fold and 5-fold over random AP operation mode selection. Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints, which introduces a decaying violation budget to balance performance optimization and compliance with time-varying constraints. Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC, which proposes a novel UAV-assisted MEC system that provides both computational resources and energy support for ground IoT terminals. Graph Neural Network Aided Deep Reinforcement Learning for Resource Allocation in Dynamic Terahertz UAV Networks, which achieves resource efficiency maximization in dynamic THz UAV networks. GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks, which leverages a generative flow network to sample favorable solutions and achieve 20% performance gains against benchmarks.