Towards Self-Sustainable and Intelligent WSNs: A Survey of Energy Harvesting and Cognitive Radio Technologies

Abstract

: Wireless Sensor Networks (WSNs) have gained significant attention due to their wide range of applications in environmental monitoring, smart agriculture, healthcare, industrial automation, and Internet of Things (IoT) systems. The current WSNs have limitations such as limited battery capacity, low network lifespan, spectrum scarcity, scalability problems, and poor communication quality. To address the constraint of limited energy, several technologies have been proposed for energy harvesting (EH) to ensure an operation of self-sustainable networks, which makes possible to tap into various sources of environmental energy, including sun, vibration, or radio frequency signals. Cognitive Radio (CR) technology has similarly proved to be a beneficial approach to spectrum utilization through dynamic spectrum access and intelligent channel selection. EH and CR technologies are combined to form Energy Harvesting Cognitive Wireless Sensor Networks (EH-CWSNs), which enable better energy efficiency, better spectrum utilization and longer network lifetime. However, the routing complexity, clustering efficiency, spectrum sensing, scalability, communication reliability and resource management are some of the problems that still remain with EH-CWSNs. This survey provides an exhaustive analysis of the existing routing protocols, clustering, optimization and energy management techniques in WSNs and their benefits and drawbacks, with emphasis on EH-WSNs, Cognitive WSNs, and EH-CWSNs. In addition, some current challenges and future research directions are outlined for the development of efficient and intelligent wireless sensor networks.