## 1 Introduction  
The introduction of cognitive radios (CR) onto wireless sensor network (WSN) nodes promises not only to improve the utilization of unused spectra, but to solve the crowded spectra issue. It also carries along with it various benefits, which include: the enhancement of channel reliability used under bursty traffic, lifetime maximization via adaptive power and bandwidth allocation and making possible the easy integration of WSN into the heterogeneous Internet of things (IoT) [1,2]. As such, the cognitive radio sensor network (CRSN) paradigm is gaining more attention in the research community [3]. Implementing effective data transmission schemes across the CRSN is core to harvesting these benefits of the CRSN paradigm [4, 5, 6]. Although research in routing for CR _ad hoc_ networks has been active in recent times, the issue of providing quality of service in terms of reliability still remains a crucial open issue that needs to be addressed [7]. This issue becomes more challenging when the unique resource constraint characteristics of CRSN are to be considered [8]. For CR _ad hoc_ networks (CRAHNs), the dynamic nature of channel availability between the source and sink node has made on-demand routing approaches favored over other approaches [9]. Among reviewed on-demand routing protocols for CRAHNs, routing protocols that modify the _ad hoc_ on-demand distance vector (AODV) are the most popular ones [10]. However, although the AODV scheme provides an easy way to manage the dynamic nature of the network topology by securing a path to the sink before commencement of the data-routing stage, the common control channel (CC) concept, which is mostly utilized in AODV-based CRAHNs, does not provide a framework for confirming node-to-node instantaneous link quality at the point of packet transmission between nodes [7]. This is because the next hop decision is usually concluded via CC based on the joint route and spectrum selection scheme implemented for most CRAHNs [6]. This can lead to serious adverse quality of service (QoS) effects, especially for resource-constrained geographical forwarding schemes [11]. Geographic routing protocols are classified as efficient in wireless networks specifically for two reasons: (i) transmitting nodes only need to know the location information of their direct neighbors in order to forward packets, and hence, the state stored is minimum; (ii) discovery floods and state propagation are not required beyond a single hop. Thus, they conserve energy and bandwidth. In the greedy forwarding mechanism, which is the main component of geographic routing, each node forwards a packet to the neighbor that is closest to the destination. This strategy can only be classified as an efficient, low-overhead method of data delivery based on three assumptions, namely: (i) accurate localization; (ii) sufficient network density; and (iii) high link reliability independent of distance within the physical radio range. However, while the first two assumptions may be acceptable in some cases, the assumption concerning highly reliable links is unlikely to be valid in any realistic deployment. This is because of the existence of a large region of transition where the link quality has high variance, including both good and highly unreliable links [11]. Consequently, experimental studies on wireless _ad hoc_ and sensor networks [12,13], have shown that wireless links can be highly unreliable and that this must be explicitly taken into account when considering higher-layer protocols. While considering the above, in searching for the next hop in CRSN geographical forwarding based on the joint route and spectrum selection scheme, the choice between two criteria usually arise: (i) the stipulation of the closest node to the transmitting node criterion; or (ii) the stipulation of the closest nodeto the sink criterion.