The process of spectrum sensing is an essential task in various applications concerning modern communication systems. The hottest topic at the moment is related to cognitive radio applications, where the spectrum around a mobile user equipment (UE) is sensed to discover free portions of frequency, which could be used ulterioly. As long as primary users are not using the spectrum, secondary users can use it. The overall spectrum usage can therefore be optimzed based on the dynamic allocation of the spectrum. Furthermore, different software defined radio related applications might be enabled by gaining knowledge of the instantaneous spectrum allocation. For example, for mobile UEs a careful power management is essential. Despite this fact, quite an amount of energy is wasted in today's UEs' analogue (AFE) and digital frontends (DFE). The latter are engineered for extracting the wanted signal from a spectral environment defined in the corresponding communication standards with their extremely tough requirements. These requirements define a worst case scenario still ensuring reliable communication. In a typical receiving process the actual requirements can be considered as less critical. Knowledge about the actual environmental spectral conditions allows to reconfigure both frontends to the actual needs and to save energy. In this paper we give an overview on recent advances in low complex spectrum sensing approaches used for the described tasks, focusing on energy detection based methods.