## 1 Introduction
### Motivation  
Many studies concerning energy management problems for energy-harvesting nodes have been conducted, primarily to maximize a system's throughput [2; 3; 4; 5; 6]. For example, [PERSON] and [PERSON] [2] proposed a joint design of a spectrum sensing policy and a detection threshold to maximize total expected throughput under energy constraints. [PERSON] et al. [4] examined the two-dimensionalmaximum stable throughput region for a simple cognitive system comprising two source-destination pairs. [PERSON] and [PERSON] [6] designed an intelligent decision-making (IDM) model for wireless sensor networks, which allows the sensor node to obtain energy from the Sun, and thus preserves its battery energy in an outdoor environment. [PERSON] et al. [7] studied the optimal sensing duration to maximize achievable throughput for a secondary network while sufficiently protecting primary users. There is research that analyzes optimal transmission power and density of secondary transmitters to maximize secondary network throughput under the constraints of a given outage-probability [8]. In addition, the work in [9] explored a multiple-input multiple-output (MIMO) technique for collaborative spectrum sensing for the distributed detection framework in cognitive-radio scenarios; this paper focuses on the reporting channel in a spectrum-sensing context and exploits the results from decision fusion to improve probability of detection. In addition, cognitive radio networks (CRNs), like any modern communications system, should guarantee the privacy of the data traveling through the network [10]. However, due to its open and random access nature, wireless communications in CRNs is susceptive to security threats targeting the physical or media access control layers (e.g., passive eavesdropping or radio frequency (RF) jamming). For that reason, a remarkable number of contributions focus mainly on security technologies for CRNs [11]. In particular, [PERSON] et al. [12] presented physical layer approaches to defend against security threats in CRNs. The authors first introduced a MIMO technique that guarantees a low probability of interception, and that enhances the confidentiality of the network; then, they proposed an identified scheme based on channel responses to defend against primary user-emulation attacks. [PERSON] et al. [13] studied channel-aware decision fusion rules to classify the presence of a (either distributed or co-located) multi-antenna jamming device in wireless sensor networks. Moreover, physical layer security in CRNs has been widely studied to secure wireless transmissions, especially in the presence of a hidden eavesdropper [14; 15]. Besides this, keeping the data classified from prying eyes by using encryption techniques is one of the most feasible solutions to maintain security; but, in reality, it is not easy to implement conventional encryption techniques in CRNs, since the networks have constrained resources (e.g., limited energy or memory). As a consequence, encryption techniques such as symmetric and asymmetric key algorithms are not preferred for data protection in CRNs. Nevertheless, in modern CRNs, wireless energy harvesting technology can ensure the energy autonomy of the network by using a small rechargeable battery integrated with an energy harvester, thus providing the SUs with redundant energy to improve data security. Therefore, protecting data using encryption methods still attracts a lot of interest in the research community [16; 17; 18]. To illustrate, [PERSON] [19] identified numerous security threats to cognitive wireless sensor networks and the defense mechanisms against these vulnerabilities by selecting the most appropriate cryptography algorithm for each class of attack. Recent work proposes an energy-efficient data encryption scheme for an SU powered by an energy harvester to decide its operation mode (e.g., stay silent or transmit encrypted data) in the current time slot [20].