## 7 Observations and Recommendations
### Summary Comparison  
As can be seen in Table 3, the parametric methods [34; 35; 36] all require prior knowledge of the data's density distribution. This means that these methods aren't flexible when considering the lack of control over the knowledge formulation process. All three schemes were found to have high detection accuracies and two of the schemes had a data recovery mechanism when an anomaly was detected. The majority of the discussed schemes did not consider data prediction, in fact only two of the non-parametric schemes did making it a total of four across all schemes. An important consideration is that all the schemes that included data prediction were specifically designed to detect data integrity anomalies which could have been the contributing factor. Going back to the parametric methods, the resource constraints of IWSNs were only considered in one of the methods but they all have theoretically low computational overheads thus making them feasible for these applications. IWSNs are however generally deployed in dynamic environments but these parametric methods are suitable for static environments where the data's density distribution is not likely to change over time. The non-parametric methods are thus better suited for IWSNs because they are generally more robust but this comes at the cost of a higher computational overhead.  
This paper focussed on non-parametric schemes that employed machine learning to detect anomalies. Of the KNN group of schemes discussed only [41] was specifically designed for resource constrained devices. The other two schemes discussed [42; 43] did not consider the resource constraints and the former required large amounts of training data. For the non-parametric schemes, prior knowledge was indicated only if the scheme required a substantial amount of labelled training data. The scheme proposed in [41] achieves its efficiency through distributed computing and also uses unsupervised online learning thus eliminating the need for prior knowledge. In Table 3 it is however noted that the practical implications of the scheme were not considered. This is because the efficiency of the scheme was only considered theoretically and not backed up by any experimental data in the paper. All the non-parametric schemes recorded high detection accuracies with the exception of those proposed in [50; 53]. The issue of high detection accuracies is however contentious and will be discussed in the next section.  
Considering the SVM schemes [45; 46; 47] the practical implications were only considered in the one proposed in [45] which is also the only one that employs online learning techniques. However, like the one in [41], the scheme was not specifically evaluated for a resource constrained environment as the practical feasibility was not backed up by any experimental data. Of the three ANN schemes [50; 51; 52] discussed two of them considered the practical implications and one of them required a substantial amount prior knowledge. Most of the remaining schemes required prior knowledge with the exception being the only GA scheme that considered the practical implications [54]. When considering all the non-parametric schemes just over a third of them required little to no prior knowledge and less than a third included substantial practical considerations. It is thus evident that conceptually there have been many proposed anomaly detection schemes for IWSNs that look very promising for this growing field. The practical feasibility has however been generally neglected in the literature and should start being given more attention going forward.