## 8 Conclusions and Research Challenges  
This paper has presented the most relevant works in Data Centric Storage (DCS) for Wireless Sensor Networks (WSNs) including routing, multi-replication, storage policies, _etc._ Table 3 summarizes the main proposals and their contributions in the field of Data Centric Storage. Figure 9: Extra communication cost produced by ToW in front of QAR when consumption dominates production in Y1 axis. Number of replicas used for each approach in Y2 axis (A = 1,000 \(\times 1,000,N=5,000,Tx=60,Iterations=100)\). However, we believe that there are still some research challenges that need to be addressed. A first research topic that still presents some challenges is the _storage analysis for DCS systems_ (see Section 4.). Storage in DCS has been studied in very few works [27, 28] in the literature. These papers basically say that when the home node is saturated a new one is chosen, and later another one and so on. However, they do not consider that usually sensor data is only useful during a given time window. In addition, there could be many different event types in a sensornet, thus there are some probability that a given node is selected as home node (or replica) for several applications. Therefore, it would be very interesting to study the maximum number of applications (event types) that could be deployed at the same time in a WSN due to storage constrains. In the area of multi-replication (Section 5.) we find different structured replication techniques, mainly represented by multi-replication GHT and ToW, but they are not adaptive enough due to its \(4^{d}\) replication scheme. In order to improve the adaptivity we have presented our QAR replication model, which increases the adaptivity by using a \(d^{2}\) replication scheme. In addition, QAR outperforms ToW and multi-replication GHT by reducing the overall network traffic (that has a direct impact on energy consumption and sensornet lifetime). However, all these works assume a square sensornet and are tied to a grid structure that requires some previous knowledge on grid number and size. We believe that an _unstructured replication scheme_ that selects the optimal number of replicas without depending on a grid structure would reduce the complexity of the structured replication proposals, as well as improving its performance and adaptivity. Figure 10: Extra communication cost produced by ToW in front of QAR when production dominates consumption in Y1 axis. Number of replicas used for each approach in Y2 axis (A = 1,000 \(\times 1,000,N=5,000,Tx=60,Iterations=100\)). There is an inherent problem to DCS systems that has been only mentioned in [34] but not studied. This problem is the _unfair energy consumption distribution that DCS creates among the sensors_ in static WSNs. It is clear that home nodes (in standard DCS scenarios) and replication nodes (in multi-replication DCS scenarios) incur in higher energy expenditures than other nodes within the network. Then, we believe that the role of the home node should change over the time in order to balance the energy consumption among all the nodes in the sensornet. We think that improving this energy balance will lead to extend the sensornet lifetime. Obviously, changing the replicas over the time balances the energy expenditures among the sensors, but it is not free of cost. Changing the replication nodes without information loss requires the data stored in the old replicas be moved to the new ones. Therefore, it should be studied what is the impact of this additional cost on the sensornet lifetime. In addition, a dynamic DCS system requires to define new mechanisms and protocols, and in order to be practical they should not add much complexity. It must be highlighted that this research challenge is only interesting in static WSNs, because if the sensors are mobile nodes, the home node (or replicas) changes over the time due to node mobility so no additional mechanism needs to be applied.