A sampling algorithm for intermittently connected delay tolerant wireless sensor networks

This work presents a sampling solution applied to intermittently connected delay tolerant wireless sensor networks (ICDT-WSNs). In such networks, when the storage capacity of a node is limited compared to the amount of data to collect, it is common to apply a packet drop strategy based on network layer parameters only. However, such strategies are not suitable for monitoring applications of WSNs where data quality is essential. Alternatively, we propose a data-aware drop strategy which applies a sampling algorithm over the collected data in order to reduce the amount of stored data while keeping an overall data quality to represent the monitored area. In order to evaluate our solution, we executed and compared it with drop strategies in literature. We modeled and simulated scenarios where different events occur. The results show that the sampling algorithm is, approximately, twice better than common drop strategies in all evaluated scenarios.