## 8 Conclusions and Future Work  
We first discussed and analyzed the various types of jamming attack models, the determinant metrics for jamming detection, and the existing methods of jamming detection as applied to the wireless sensor networks. We also discussed how our approach to the problem differed from the existing ones from three angles: (1) the scope of the lethality of the jammer being enlarged to include military jammers, (2) the existing approaches consider only two discreet levels of jamming, jammed and not jammed; where as we consider that decision about whether a node is jammed or not jammed is a fuzzy one and accordingly, aim to grade different node as per their jamming indices which suits the information war environment allowing various options to the war- zone commander (or, base station) in adopting different policies with respect to victimized nodes, and (3) the decision for jamming detection is taken by the nodes themselves in the existing methods, which we consider not feasible due to the resource constraints of the WSN nodes and their ineffectiveness in communicating with other nodes during jamming, and accordingly, we choose to do all processing and decision making at the base station on a holistic picture. Having done so, we then selected packets dropped per terminal (PDPT) and signal-to-noise ratio (SNR) as the input to our fuzzy inference system based on Mamdani model which gave the jamming index (JI) of various nodes as output. This output is further evaluated on an overall picture based on the neighborhood of the nodes. We then evaluated the system performance through the true detection ratio (TDR) and false detection ratio (FDR), and then we compared the performance with the method proposed by [PERSON] _et al._ and found that our performance is better in most of the cases, matching in few cases, and not as good as the others in rare cases. We are unable to compare our performance with those of other existing models because their quantitative performance evaluation parameters are not publically available. We also analyzed the performance of the proposed approach (centralized or base-centric approach) against the existing approaches (decentralized or node-centric approach) and found that the proposed approach is matching with the existing approaches in terms of speed and energy consumption and is better in terms of accuracy. Our model has the unique advantages of providing flexibility to the battle field commander (base station) in resource (node) utilization through grading the nodes with jamming indices, and of survivality in an information war, as the model is procedure-based as against protocol-based, with the latter involving inter-node communication in a jamming environment which may not be always possible. The proposed method has the additional advantage of being able to discriminate various types of jamming attacks without getting into the complexities of digital signal processing which must be avoided as they are not practicable in the WSN scenario in an information warfare environment.  
We find that our algorithm for confirmation of jamming attack on a node through '2-means clustering' of node neighborhood can be refined for its performance with respect to the edge nodes, especially those in the corners. We understand that this can be done by addition of steps for discriminating edge and corner nodes from the rest and allotting various allowances to them for loss of prospective jammed or un-jammed neighbors in our algorithm. We plan to undertake this task as our future work.