.png){kind=icon}
返回
An efficient moving object tracking framework for WSNs using sequence-to-sequence learning model
.png){kind=icon}
PRE
AI
DOI：10.1007/s12083-021-01157-8.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Target tracking
Sensor data dissemination
Trajectory prediction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.6
论文数:
2.2K
被引数:
2.9K
机构
引用论文
分享收藏THE ASSOCIATION OF HIGH AMBIENT TEMPERATURES WITH INCIDENT END STAGE KIDNEY DISEASE IN THE UNITED STATES: AN ECOLOGICAL STUDY高温环境与美国人终末期肾病发生的关系：一项生态学研究
PREAI
分享收藏Counter-Based Broadcast Scheme Considering Reachability, Network Density, and Energy Efficiency for Wireless Sensor Networks无线传感器网络中考虑可达性，网络密度和能量效率的基于计数器的广播方案.png)
SENSORS
OAAI
SENSORSIF3.5
OAAI分享收藏Localización ecodirigida del cáncer de mama no palpable y del ganglio centinela con 99mtecnecio-coloide de albúmina乳腺不可触及癌的超声引导定位及99mTc-白蛋白胶体标记的哨兵淋巴结Radiología
RadiologíaIF0
PREAI
分享收藏
查看更多内容