# An Energy-Efficient Game-Theory-Based Spectrum Decision Scheme for Cognitive Radio Sensor Networks
## 2 Related Works  
To the best of our knowledge, at the time we proposed this scheme, there was no exact related work in the field of spectrum decision method for CRSNs. Nevertheless, there are spectrum decision methods proposed for general/other types of CR networks.  
Surveys on spectrum decision in cognitive radio networks and CRSNs are presented in [25] and [26], respectively. Several works deal with spectrum-related management for CRSNs. New parameters to support channel assignment are proposed in [27;  
based on game theory is proposed in [35]. While a centralized approach is plausible because there is at least one sink in a CRSN, it does not cope well with spectrum heterogeneity over time and space. The result of centralized spectrum allocation may not be optimum for every sensor node, not to mention the additional overhead cost involved.  
There are some works on spectrum decision methods for usual cognitive radio networks (except for CRSNs). An analysis of load-based, interference-based, and joint load- and interference-based decision schemes showed that basic metrics were able to improve the performance of cognitive ad hoc networks, though only in certain cases [36]. Novel parameters for spectrum decision were proposed, such as a request index to represent the quality of service requirements of SUs [37], channel usage state [38], and outage probability [39]. While those works that used new parameters showed improved performance, the definition of parameters was rather fixed (channel usage state value was predicted based on its history, but the storage was predetermined and limited.) Online learning was used in a spectrum decision algorithm based on predictions; however, the focus was merely to calculate the probability of handover [40]. Similarly, specific purpose spectrum decision methods are proposed in [41; 42; 43], in which they were focused on time minimization and security issues (the last two works). An energy-aware spectrum decision framework is proposed in [44], where it includes an energy-monitoring unit with a predefined threshold.  
A spectrum decision framework for a general CR network, which is proposed in [45], is chosen to be the comparison work for our proposed framework, EGSD. The reasons are that: (1) it is similar to EGSD in that the comparison work is a framework (instead of a standalone algorithm or protocol) and (2) it has been highly cited by other papers. Moreover, the comparison work is a complete and detailed work with strong numerical analysis and performance evaluation results. The comparison work introduced two algorithms under its spectrum decision block: minimum-variance-based spectrum decision (MVSD) and maximum-capacity-based spectrum decision (MCSD). We only compared EGSD with MVSD because MVSD was designed to support real-time applications, which is the nature of CRSN applications. We also exclude the admission control function because, in our network scenario, there will be no new sensor nodes installed in the middle of the network operation. MVSD is applied in an infrastructure-based CR network in which the base station performs the spectrum decision (centralized approach). The CR nodes or sensor nodes perform spectrum sensing, send the results to the base station, and wait for the spectrum decision results. Interesting readers may refer to reference [45] for more details.