Toward Intelligent Operations in Energy Harvesting Cognitive Low-Power Wireless Networks: A Survey

The Internet of Things (IoT) is increasingly evolving into a more intelligent and environmentally sustainable ecosystem. However, as IoT systems proliferate, they face persistent challenges, including limited spectrum and energy resources. These challenges can be mitigated by integrating cognitive radio (CR) technology and energy harvesting (EH) techniques. CR enables wireless devices to increase their spectral resources by opportunistically utilizing licensed spectrum bands, while EH allows energy-constrained devices to become self-sustaining and extend their operational lifetime. The increasing diversity of wireless devices and data, combined with rapidly changing wireless environments and unpredictable energy availability, presents significant challenges. As a result, traditional optimization-based approaches often fall short. The advent of artificial intelligence (AI) offers a promising alternative, enabling low-power wireless (LPW) networks to become self-managing, self-sustaining, and adaptive to changing conditions through data-driven methods. However, existing surveys rarely address cognitive LPW networks in depth and often overlook the role of AI in enabling EH and smart operations. This survey fills that gap by reviewing state-of-the-art AI approaches that address key challenges in EH-enabled cognitive LPW systems, including power allocation, resource optimization, and task scheduling. We review key advancements in cognitive LPW systems, such as IoT, backscattering networks, multi-hop and relay-assisted networks, and multiple access networks. Finally, we highlight key insights from the literature and outline promising directions for future research on cognitive LPW systems that enable smart, green, scalable, and secure deployments.