|Author||Souza, Fren L. ♦ Nakamura, Eduardo F. ♦ Pazzi, Richard W.|
|Source||ACM Digital Library|
|Publisher||Association for Computing Machinery (ACM)|
|Subject Domain (in DDC)||Computer science, information & general works ♦ Data processing & computer science|
|Subject Keyword||Kalman filter ♦ Target tracking ♦ Particle filter|
|Abstract||Target-tracking algorithms typically organize the network into a logical structure (e.g., tree, cluster, or faces) to enable data fusion and reduce communication costs. These algorithms often predict the target’s future position. In addition to using position forecasts for decision making, we can also use such information to save energy by activating only the set of sensors nearby the target’s trajectory. In this work, we survey of the state of the art of target-tracking techniques in sensor networks. We identify three different formulations for the target-tracking problem and classify the target-tracking algorithms based on common characteristics. Furthermore, for the sake of a better understanding of the target-tracking process, we organize this process in six components: target detection, node cooperation, position computation, future-position estimation, energy management, and target recovery. Each component has different solutions that affect the target-tracking performance.|
|Description||Author Affiliation: Federal University of Western Para, Santarém-PA-Brazil (Souza, fren L.); University of Ontario Institute of Technology, Oshawa-ON-Canada (Pazzi, Richard W.); Federal University of Amazonas, Manaus-AM-Brazil (Nakamura, Eduardo F.)|
|Age Range||18 to 22 years ♦ above 22 year|
|Education Level||UG and PG|
|Learning Resource Type||Article|
|Publisher Place||New York|
|Journal||ACM Computing Surveys (CSUR)|
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