This paper proposes a novel multi-stage hybrid optimization framework to enhance energy efficiency, operational sustainability, and deployment cost-effectiveness in Simultaneous Wireless Information and Power Transfer (SWIPT)-enabled Wireless Sensor Networks (WSNs) for Internet of Things (IoT) applications. The framework integrates Ant Colony Optimization (ACO) for optimal cluster formation, Genetic Algorithm (GA) for cost-efficient cluster head deployment, and Proximal Policy Optimization (PPO) for dynamic resource allocation. A cross-layer optimization strategy is employed to jointly address clustering, deployment, and adaptive time-switching under full-duplex SWIPT constraints. Furthermore, a reinforcement learning-based resource management mechanism enables real-time adaptation to stochastic network conditions and channel variations. Simulation results demonstrate that the proposed framework outperforms benchmark algorithms HDFA-PPO, PSO-ACO-PPO, and GA-ACO-LDSA-BSM in terms of energy efficiency, extension of network lifespan, and deployment cost. The findings confirm the framework’s effectiveness in significantly extending network lifetime, ensuring energy self-sustainability, and enhancing overall operational reliability, making it a scalable and practical solution for large-scale SWIPT-enabled WSNs in IoT environments.

Integrating wireless energy transfer (WET) and data gathering based on the mobile platforms, such as the unmanned aerial vehicle (UAV) has been recognized as a promising technique to prolong the battery lifetime of resource-constrained wireless sensors in the Internet of Things era. However, it is challenging to jointly schedule dynamic renewable energy sources and communications resources to coordinate heterogeneous performance requirements in rechargeable wireless sensor networks (RWSNs). Hence, this article researches rechargeable adaptive energy-efficient dual-mode data gathering (AED2G) using renewable energy sources. First, considering the limited endurance of UAV and the uncertainty of renewable energy harvesting, a life-expectancy-balance-based AED2G strategy is proposed for optimizing the communication energy efficiency of the fixed data gathering (FDG) and mobile data gathering (MDG). Then, considering WET and MDG, the synergy payoff function of rechargeable MDG (RMDG) is designed, and the corresponding synergy payoff maximization problem is established. The problem is nonconvex due to the coupling of MDG and WET, so it is decomposed into two layers to be quickly solved by the designed hierarchical decomposition framework. The simulation results prove that our algorithm can efficiently use renewable energy sources, whether in FDG or RMDG mode, thereby improving the sustainability of RWSN.

Wireless power transfer (WPT) based on mobile platforms, such as unmanned aerial vehicle (UAV), has been recognized as a promised technique to prolong battery lifetime of resource-constrained wireless sensors in the Internet of Things (IoT) era. However, it is challenging to collaborate multiple mobile wireless chargers (MWCs) for coordinating heterogeneous energy requirements of massive rechargeable wireless sensors, where the efficient optimization of quantitative collaboration utility among MWCs is difficult. Hence, this article investigates the adaptive payoff balance among MWCs for rechargeable wireless sensor networks (RWSNs). First, the collaborative wireless powered system based on charging sectors control among MWCs is proposed. Then, the charging payoff function and the corresponding optimization problem for maximizing the minimum payoff are designed, and it is decomposed into two layers by the hierarchical decompose method to be solved quickly. In the bottom layer, the payoff of each MWC with the given charging sector is maximized by the convex optimization theory. According to the payoff feedback of the bottom layer, intelligent charging sectors allocation is realized by the deep reinforcement learning. The simulation results show that our algorithm can ensure efficient energy allocation of any single MWC, and the overall utility of collaborative wireless powered system on this basis can be optimized by rationally allocating charging sectors, which significantly improves the sustainability of RWSN.

In recent years, wireless smart sensors powered by solar energy have been widely deployed to ensure the green and sustainable operation of remote industrial systems monitoring. Such devices can offload computing tasks locally or using the edge server by transmitting the raw data wirelessly. Since random renewable energy harvesting has a detrimental effect on the energy balance of nodes in these green rechargeable wireless sensor networks (RWSN), the rational synergy between terminal-edge collaborative tasks offloading (TECTO) and network topology optimization (NTO) is of great significance for improving the sustainability. Therefore, this article presents a learning-based terminal-edge collaborative energy-efficient routing algorithm. First, a system model is developed to integrate TECTO and NTO, and the original problem is decoupled into two layers. Then, the NTO layer is aimed at quickly generating an energy-efficient network topology by variable cycle block coordinate descent method based on the greedy strategy. Finally, the TECTO layer adopts deep reinforcement learning based on the dynamic baseline to understand the energy efficiency feedback law of the NTO layer and rationally adjusts the TECTO scheme. The simulation results show that the presented algorithm can reasonably generate the network topology and TECTO scheme according to the node's energy state change and efficiently consume the renewable energy distributed in the green RWSN, which significantly enhances its sustainability.

Abstract In order to maximize the work efficiency of wireless mobile charger, a payoff‐maximization‐based adaptive hierarchical wireless charging algorithm for mobile charger is proposed. Based on the mesh structure and multi‐node charging technology, the recharging optimization for massive devices is modelled as a problem of payoff maximization. According to energy allocation, anchor point deployment and time allocation, we decompose it into three layers by the hierarchical decomposition method to obtain optimal solution quickly. The process of energy allocation and anchor point deployment in each mesh is optimized in the first two layers based on Karush–Kuhn–Trucker (KKT) condition and greedy strategy, respectively. Based on the feedback of the first two layers, the most complex problem of time allocation in the last layer is solved by our innovative gain recall mechanism. The trade‐off between the number of recharged devices and recharging time in each cycle can be achieved by only charging the devices in the meshes which are without recall gains. The simulation results prove our algorithm can adaptively adjust the ratio of moving time to recharging time in a fixed cycle, and mobile charger can always work in efficient recharging positions, whose effect is exploited utmost.

The increase of power consumption around the world puts forward higher requirements for the robustness of the power grid. As a critical application of the smart grid, demand response (DR) has significant impacts on the stable operation of the grid. The source–grid–load system (SGLS) in Jiangsu Province of China has implemented three different types of DR. This study aims at promoting the survivability of the DR communication network, which can enhance the reliability of DR. In response to concurrent dual‐link failure, the authors first present a capacity expansion algorithm with the minimum distance to enhance the topology of the DR communication network. Next, they design a multipath routing algorithm based on pre‐estimation to configure multiple paths for every DR terminal according to the characteristics of the DR service. Then they propose a link capacity sharing algorithm for multipath to reduce the communication network cost for DR. Simulations demonstrate the proposed algorithm can improve survivability and reduce the cost of the DR communication network. Furthermore, they compare the multipath configuration algorithms in different topology enhancement schemes, which can give some suggestions on the construction of SGLS in the future.