# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 3 Game Theory-Based Routing Protocols in WSNs
### Routing Protocols Based on Noncooperative Games
#### 3.2.5 Energy-Aware Trust Derivation Scheme
If node \(j\) was a neighbor, it might unicast a trust reply to source node \(i\) using a reverse route. The decision of node \(j\) to reply or not depended on a dilemma game, which is explained later in this section. After getting the recommendation, the source node \(i\) computed the trust value by joining the direct trust with the indirect trust. At this stage, node \(i\) decided if node \(j\) could be trusted based on the obtained computed result. This routing scheme considered recommendations from the neighbors of the evaluated node. This is mainly because the malicious behaviors of a node can be best observed by its neighbors and this mechanism can reduce network overhead. The algorithm applied for trust computation was beyond the scope of this routing scheme [32]. The authors of [32] assumed that a robust trust evaluation algorithm was implemented for trust computation. However, an efficient design was required to ensure that the trust information obtained from a certain set of selected neighbors fulfilled the desired security requirements. A game-theoretic approach was considered to deal with this problem. The TDDG was designed with \(N\) nodes, where \(N\) presents the number of specified evaluated nodes' neighbors, excluding the evaluating node. There were two strategies for the participating nodes: \"reply\" and \"not reply\". \"Reply\" refers to the event that nodes replied to the evaluating node during the trust request reception. \"Not reply\" refers to the strategy where the nodes would ignore the trust request after receiving it. Assuming that \(k\) is the minimum number of recommendations needed to fulfill the security requirements, the network was regarded as secured if the number of recommendations was greater than \(k\). The nodes participating in the TDDG did not include themselves in the game if they had low energy. There could be two cases in the TDDG. Considering a case where \(k\) = \(I\), the security requirement could be met if any node sent a trust reply. The pay-off matrix, probability of sending trust reply, strategic form, and Nash equilibrium were derived for this case. There could also be a case where \(k>1\). The strategic form and Nash equilibrium for TDDG in this case were derived too. Two important parameters for this scheme were the utility and cost for an arbitrary node for trust reply. From the simulation results in [32], it was concluded that the energy consumption increased as the ratio of the above mentioned two parameters increased. The reason for this is that the high ratio of utility and cost encourages nodes to reply to a trust request packet, which increases network overhead and congestion resulting in long network latency. Figure 5: Process of trust derivation. Technical strengths:  
* Security of a WSN is considered with highest priority;
* Performance analysis with security attacks like bad mouthing [65] and distributed denial of service (DDoS) [66]. Technical weaknesses:
* No performance comparison with the existing state-of-the-art algorithms;
* The trust derivation problem is hop-limited. This can lead to a contradiction. Flooding in an uncontrolled and random manner may lead to energy wastage and latency. For this, hop-limited flooding is useful, but it may lead to reduced trust information collection. The authors of [32] did not mention any solution for this contradictory situation. Applications:
* Highly suitable and applicable for military applications. Possible future improvements:
* Introducing more routing metrics for performance evaluation;
* Comparison of the performance analysis with any existing state-of-art routing protocol;
* Introducing a new intelligent method of broadcasting other than hop-limited flooding to avoid energy wastage and facilitate efficient trust information gathering simultaneously.