## 2 Related Works  
The existing continuous object detection and tracking methods have three classes. The first class comprises the cluster-based methods. Generally, the number of deployed sensors is high. It causes sensors to consume much energy on data propagation. Therefore, this class divides sensors into several small clusters. Every cluster selects a cluster head (CH) to coordinate the sensors' data communication and collection. Then, the CH aggregates data and delivers them to the sink to reduce the sink's data receiving requests. The Dynamic Cluster Structure for Object Detection and Tracking algorithm [1], denoted as DCSODT, proposed by [PERSON] et al., is a cluster-based method. DCSODT adopted a location-based clustering method to trace the continuous object. At first, DCSODT elects the boundary sensors according to the sensing status of the sensors. Next, it verifies the states of the one-hop neighbors of these boundary sensors to determine the cluster. When the continuous object shifts, DCSODT will update the boundary sensors and reform the sensors' cluster. Although sensors in DCSODT reduce the numerous data transmission to the sink via the cluster structure, they still need to frequently exchange messages with their one-hop neighbors to update the cluster structure. [PERSON] et al. also proposed a Continuous-Object Detection and Tracking Algorithm, denoted as CODA [2]. CODA dynamically clusters the sensors to detect and trace the continuous object. At first, it divides the sensors into multiple clusters. The CH of each cluster evaluates the locations of the newly joined sensors. Next, CODA models the sensors as the vertices in the geometrical plane and then applies the convex hull algorithm to select the boundary sensors. For reducing the energy consumption of the sensors, the clusters that included any boundary sensors can only send new data to the sink. CODA continuously performs the clustering procedure to update the boundary sensors. The message exchanging during the continuous clustering operation will generate great penalties in communication. Using the planarization algorithms to detect the boundary area is the role of the second class. The widely used planarization algorithms for constructing the boundary area of the continuous object are Gabriel Graph (GG) [5], Relative Neighborhood Graph(RNG) [6], Delaunay Triangulation [7], k-Localized Delaunay Graph (_LDel\({}^{k}\)_) [8], and Yao Graph (YG) [9]. Generally, the planarization algorithms treat the sensors as vertices on the planer graph. [PERSON] et al. compared the impact of GG, RNG, _LDel\({}^{k}\)_, and YG to analyze the accuracy of detecting the continuous object [16]. They concluded that _LDel\({}^{k}\)_ has the best results. They classified the sensors into inner nodes and outer nodes according to whether the sensors detect the event or not. Nodes create a coarse boundary region to predict the boundary of the continuous objects. Usually, these methods focus on building the geometry planer graph and ignore the practical communication range of the sensors. The third class uses the mobile sensors to adjust the initially deployed positions and then computes a more accurate boundary area. [PERSON] et al. proposed a novel boundary area detection technique named DeGas scheme [13]. DeGas also treats sensors as the vertices of a graph and divides sensors into the inner boundary node (IBN) and the outer boundary node (OBN). Each IBN constructs a link to each one-hop OBN neighbor. This kind of link is named critical edge (CE). The DeGas scheme chooses a CE to explore the connection edges between IBNs and OBNs to build an enclosed area (also called a boundary face) with a mobile car. The proposed method tries to avoid deploying too many redundant sensors. [PERSON] et al. extended the idea of the DeGas scheme to detect the dangerous area of toxic gases [17]. This method considers the scenario that sensors may be out of function after operating for a while.