## 1 Introduction  
The number of mobile and Internet of Things (IoT) devices continues to grow at an unprecedented rate [1]. In addition, these systems increasingly have better performance, so new and more demanding applications and services are continuously appearing. These growth trends entail a substantial increase in wireless data traffic, and therefore, spectrum occupancy, which in turn poses new and important challenges to overcome. One of these challenges is to achieve highly efficient and reliable communications in increasingly complex, heterogeneous, and dynamic scenarios where many different radio systems coexist. To this end, multiple cognitive radio (CR) techniques are being proposed to allow the devices to sense and analyze the spectrum, and based on that information and on their previous experience, make decisions about its use and adapt their communication parameters accordingly [2, 3]. On the other hand, applications and services increasingly demand a more comprehensive understanding of the environment, i.e., a higher level of information is sought through the processingof raw sensor data [4; 5]. Given the spectrum scarcity problem, it is not appropriate to perform this processing in the cloud since it would generate latency problems and massive network traffic due to the data uploading. For this reason, and due to the improvements in the end-devices features and performance, edge computing is gaining importance in areas like the IoT [6; 7]. This paradigm argues that computing and storage capabilities must be placed where the data is generated, i.e., in the end-devices, which must be responsible for all or at least some of this processing. This approach allows for faster response times and reduces communication overheads, which in turn means energy savings. However, important CR and processing tasks have a strong dependence on the hardware architecture and resources of the devices. For example, most of the CR techniques proposed in the literature are based on software-defined radio (SDR) platforms since they allow for direct access to the in-phase and quadrature (I/Q) samples of the baseband signals and have a great radio flexibility. These features offer many more possibilities to sense and adapt to the radio environment than those of commercial off-the-shelf (COTS) transceivers, whose rigid application-specific integrated circuit (ASIC) implementation severely limits experimentation with the lower layers of the communication stack [8]. Nevertheless, current SDR platforms have not been designed from the perspective of battery-powered end-devices, so they rely on high-performance chips, whose power consumption and price are very high [9]. In addition, since the complexity and latency restrictions of processing tasks are continuously increasing, the use of hardware solutions for their implementation, such as FPGAs, becomes more common [10]. However, in the same way, they usually rely on high-performance development platforms [11], with the same aforementioned drawbacks, or on custom solutions for specific applications [12; 13]. Current end-devices, such as TelosB [14], MICAz [15], or YetiMote [16], do not usually have those hardware processing resources and are equipped with COTS transceivers, thus achieving a very low power consumption. This permits experimentation in real scenarios with long-term deployments formed using numerous units, which in turn allows developers to evaluate how their protocols or techniques scale. However, this system architecture limits CR and edge computing research in areas of great interest, such as IoT, wireless sensor networks (WSNs), or mobile sensing, because of the significant architectural constraints it presents. Consequently, this work aims to address the lack of an appropriate platform for these research areas. Specifically, our contribution is a wireless experimental platform that simultaneously addresses the energy-efficiency requirements of end-devices and the hardware flexibility demanded by the current CR and edge computing challenges.