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.

无线传感器可以从物体反射的光，物体产生的噪声或所散布的气体分子中检测出物体。但是，跟踪移动物体需要无线传感器执行高频感应和数据传输，这会消耗更多的能量。为了节省能量并延长无线传感器网络的寿命，同时有效地跟踪运动对象，本文提出了一种框架，该框架使用序列到序列学习（Seq2Seq）模型预测运动对象的轨迹，并且仅唤醒传感器通过特殊设计的控制包，它们落在运动对象的预测轨迹之内。该框架使用DV-Hop（跳到锚点的距离矢量）作为虚拟坐标，从而消除了使用GPS定位要调用的用于跟踪运动对象的传感器的依赖性。该框架将对象的运动轨迹转换为级联双曲线的序列，并使用DV-Hop约束对双曲线进行编码。包含这些约束的控制包将禁止不在轨道上的传感器进行重新广播，并禁止唤醒/睡眠信号来控制传感器的动作。提出的Seq2Seq模型直接预测目标的下一个轨迹，并输出一条控制消息，该消息可以沿预测的轨迹进行路由。与先预测目标轨迹然后使用双曲线等几何对象对轨迹进行编码相比，提出的Seq2Seq模型减少了对地理空间轨迹进行编码的计算时间。而且，所提出的框架通过仅发送跳的信息而不是GPS值来保留位置匿名性。