Anurans (frogs or toads) are commonly used by biologists as early indicators of ecological stress. The reason is that anurans are closely related to the ecosystem. Although several sources of data may be used for monitoring these animals, anuran calls lead to a non-intrusive data acquisition strategy. Moreover, wireless sensor networks (WSNs) may be used for such a task, resulting in more accurate and autonomous system. However, it is essential save resources to extend the network lifetime. In this paper, we evaluate the impact of reducing data dimension for automatic classification of bioacoustic signals when a WSN is involved. Such a reduction is achieved through a wrapper-based feature subset selection strategy that uses genetic algorithm (GA). We use GA to find the subset of features that maximizes the costbenefit ratio. In addition, we evaluate the impact of reducing the original feature space, when sampling frequencies are also reduced. Experimental results indicate that we can reduce the number of features, while increasing classification rates (even when smaller sampling frequencies of transmission are used).
Wireless Sensor Networks consist of a powerful technology for monitoring the physical world. Particularly, innetwork data fusion techniques are very important to applications such as target classification and tracking to reduce the communication burden in these constrained networks. However, the efficiency of the solution can be affected by the data correlation among several sensor nodes. Thus, the application of value fusion (for clusters of nodes with correlated measurements) and decision fusion (combining the local decisions of the clusters) is a common strategy. In this work, we propose an algorithm for properly selecting the groups of nodes with correlated measurements. Experiments show that our algorithm is 30% better than a solution that considers only the spatial coherence regions.
Wireless Sensor Networks (WSNs) are commonly employed for environmental and wildlife monitoring. In these scenarios, mobile robots with specialized sensing, processing and actuation abilities may be employed to investigate relevant events in place. However, driving the robot to the event place is not a trivial task in the typical case where sensor nodes do not have positioning sensors such as GPS. In this work we propose a novel navigation algorithm for the mobile robot based solely on the Received Signal Strength Indication (RSSI) of communication packets. The proposed algorithm builds on a probabilistic signal propagation model recovered from real signal decay data, unlike alternative solutions found in literature. Simulations show a superior performance in comparison to similar related work.
Many literature papers evaluate solutions in wireless sensor networks by simulation experiments. However, little attention is given to the adequacy of the simulator propagation models to the environment in which such solutions are employed. This can lead to imprecise or inconsistent results in relation to the real world. This paper presents a methodology for adjusting the parameters of these models. In particular, we present experimental results for rainforest environments, which can be the goal of many sensor networks monitoring applications. The impact of the proposed approach is shown by evaluating a localization solution. The results show that this procedure is necessary for a higher fidelity of simulation experiments.
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