An efficient moving object tracking framework for WSNs using sequence-to-sequence learning model

Wireless sensors can detect an object from the light it reflects, the noise it causes, or the gas molecules it disseminates. However, tracking a moving object requires the wireless sensors to perform high-frequency sensing and data transmission which consume much more energy. To save energy and prol...

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Vydané v:Peer-to-peer networking and applications Ročník 14; číslo 6; s. 3915 - 3928
Hlavní autori: Cao, Xiaofei, Madria, Sanjay
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.11.2021
Springer Nature B.V
Springer
Predmet:
Communications Engineering
Computer Communication Networks
Computer Science
Data transmission
Energy conservation
Engineering
Hyperbolas
Information Systems and Communication Service
Learning
Moving object recognition
Networks
Performance assessment
Sensors
Signal,Image and Speech Processing
Special Issue on Convergence of Edge Computing and Next Generation Networking
Telecommunications
Tracking
Wireless networks
Wireless sensor networks
Target tracking
Trajectory prediction
Sensor data dissemination
ISSN:1936-6442, 1936-6450
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Popis
Shrnutí:Wireless sensors can detect an object from the light it reflects, the noise it causes, or the gas molecules it disseminates. However, tracking a moving object requires the wireless sensors to perform high-frequency sensing and data transmission which consume much more energy. To save energy and prolong the lifetime of wireless sensor networks while tracking a moving object effectively, this paper proposes a framework that predicts the trajectory of the moving object using a Sequence-to-Sequence learning (Seq2Seq) model and only wakes-up the sensors that fall within the predicted trajectory of the moving object with a specially designed control packet. The framework uses DV-Hop (distance vector of hops to anchors) as the virtual coordinate that eliminates the dependency of using GPS to locate the sensors to be invoked for tracking the moving object. The framework translates the object’s moving trajectory to a sequence of cascaded hyperbolas and encodes the hyperbolas with DV-Hop constraints. A control packet containing these constraints forbid sensors not in the trajectory to rebroadcast, and awake/sleep signals that control the sensors’ action. The proposed Seq2Seq model predicts the target’s next trajectory directly and outputs a control message that could route along the predicted trajectory. In comparison to predicting the target’s trajectory then encoding the trajectory using geometric objects such as hyperbola, the proposed Seq2Seq model reduces the computation time of encoding geospatial trajectory. Also, the proposed framework preserves the location anonymity by only transmitting the hop’s information instead of GPS values. The performance comparisons with the existing methods show an improvement in energy-saving and control message routing delay.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
USDOE
ISSN:1936-6442
1936-6450
DOI:10.1007/s12083-021-01157-8