## 2 Related Work
Studies were organized by network types (single-hop networks, multi-hop networks with relays, MIMO, cognitive networks, among others) and energy transfer strategies (including SWIPT--simultaneous wireless information and power transfer). Furthermore, they discussed routing policies and power control algorithms within these systems. They presented open challenges in integrating RF harvesting into wireless networks and emphasized the necessity of co-optimized circuit and protocol designs. Their findings are primarily qualitative, compiling proposed architectures and energy management strategies across various applications (WSN, radio frequency identification (RFID), cellular networks) and identifying emergent trends. However, they provided no quantitative performance comparison; evaluation remained descriptive, and while outlining future directions, they conducted no statistical meta-analysis of performance. [PERSON] and [PERSON] [21] focused their review on WSN with energy harvesting capabilities. They presented a comprehensive taxonomy of various ambient energy sources exploitable in WSNs (solar, thermal, vibration, RF) and detailed recent energy prediction models developed to estimate energy availability at sensor nodes. They compiled harvesting technologies aimed at extending battery life in WSNs and examined energy management strategies in self-sustainable networks. A significant contribution was their discussion of predictive mathematical models that enable anticipation of harvested energy, as maximizing harvesting requires adapting node consumption based on projected incoming energy. They also identified technical gaps and challenges: they noted that, despite progress in self-powered WSN prototypes, challenges persisted in achieving cost-effective, efficient, and reliable systems. Their conclusions underscored the need to improve energy conversion efficiency and develop smarter energy management schemes. However, similar to previous reviews, their analysis lacked a uniform numerical comparison, focusing instead on classifying and describing approaches without integrating performance data statistically. This reflects that in 2016, the field was still establishing categories and concepts, and less oriented towards quantitative meta-analyses. [PERSON] et al. [22] published a far-reaching review addressing wireless communications with energy harvesting from end to end. Responding to growing attention toward green communications, this work covered everything from energy source models to network protocols, providing a unified perspective on self-powered systems. In their introduction, they highlighted the intermittent nature of renewable sources as a challenge, and they structured their survey around several technical axes: (1) energy sources and models, (2) energy harvesting and utilization protocols, (3) scheduling and optimization algorithms for managing energy in devices, and (4) energy harvesting (EH) implementation in various network scenarios. They compiled numerous schemes proposed in the literature, for instance: energy-adaptive medium access control (MAC) protocols, resource allocation techniques in energy harvesting networks, and battery control strategies in sensor nodes. An added value of this review was its emphasis on future directions, identifying gaps such as the need for more holistic integration of energy management with communication layers. Similar to other surveys of that era, their work did not provide unified comparative metrics; instead, it technically synthesized the literature and highlighted trends. [PERSON] et al. [23] centered their review on design aspects of RFEH systems for IoT applications. They recognized that a primary barrier to implementing autonomous IoT nodes is continuous power provision and that RF harvesting emerges as a promising solution when other sources (like solar) are not viable. Nevertheless, they emphasized that harvesting energy from ambient RF signals entails significant challenges, mainly in overall conversion efficiency, operational bandwidth, and device form factor.