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Reconstructing the environment by sensory data is a fundamental operation for understanding the physical world in depth. A lot of basic scientific work (e.g., nature discovery, organic evolution) heavily relies on the accuracy of environment reconstruction. However, data loss in wireless sensor networks is common and has its special patterns due to noise, collision, unreliable link, and unexpected...
In wireless sensor networks (WSNs), since many basic scientific works heavily rely on the complete sensory data, data recovery is an indispensable operation against the data loss. Several works have studied the missing value problem. However, existing solutions cannot achieve satisfactory accuracy due to special loss patterns and high loss rates in WSNs. In this work, we propose a multiple attributes-based...
In our previous work, a node self-localization algorithm based on virtual central node (VCN) for wireless sensor network (WSN) is proposed. This method is adapted to WSN that anchor nodes present a uniform distribution in three dimensional spaces. During the localization process, by deducing a 3D special node, which is called virtual central node, unknown nodes can compute their own positions automatically...
Due to uncertainties in target motion and limited sensing regions of sensors, collaborative target tracking in wireless sensor networks (WSNs) suffers from low tracking accuracy and lack of reliability when a target cannot be detected by a scheduled sensor. Generally, actuating multiple sensors can achieve better tracking performance but with high energy consumption. Tracking accuracy, reliability,...
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