###### Abstract  
This work investigates boundary node selection when tracking a jammer. A technique to choose nodes to track jammers by estimating signal-to-noise Ratio (SNR), jammer-to-noise ratio (JNR), and jammer received signal strength (JRSS) are introduced in this paper. We proposed a boundary node selection threshold (BNST) algorithm. Every node can become a boundary node by comparing the SNR threshold, the average SNR estimated at the boundary node, and the received BNST value. The maximum sensing range, transmission range, and JRSS are the main parts of this algorithm. The algorithm is divided into three steps. In the first step, the maximum distance between two jammed nodes is found. Next, the maximum distance between the jammed node and its unjammed neighbors is computed. Finally, maximum BNST value is estimated. The extended Kalman filter (EKF) is utilized in this work to track the jammer and estimate its position in a different time step using selected boundary nodes. The experiment validates the benefits of selecting a boundary when tracking a jammer. W 201  
and active mode. The jammer blocks a communication channel for a random time and then turns to sleep mode. The time between sleep and transmit modes is unknown and is randomly selected by the jammer. The advanced level of jamming attacks use a proactive jammer. This kind of attack runs on a frequency domain. The jammer senses all possible channels. Once it detects a channel used by another node, it immediately emits a signal to jam it. The jammer adapts its frequency from one carrier to another, so it can block all channels. This type is difficult to detect because it uses the same method used in the frequency-hopping spread spectrum (FHSS) transmissions and direct-sequence spread spectrum (DSSS) transmissions. Localizing a jammer in WSNs is necessary to improve existing countermeasures [3]. WSNs are designed as multi-hop networks, in which each node forwards its collected data to the next hop node until reaching the sink node. The routing protocols need to determine the shortest path between the sender and the destination. When jamming attacks occur, the network topology is affected based on the jammer's transmission range and the jammed region. The network topology is classified into three main types based on the jamming signal [3]. Unjammed nodes are sensors located outside the jamming region and can transmit and receive data with neighbors. Boundary nodes may measure the jammer received signal strength (JRSS) directly from the jammer. They can communicate with unjammed and other boundary nodes. Jammed nodes are all sensors located inside the jamming region. These are entirely isolated from networks and cannot send or receive data from other sensors. All data sent to the jamming region fails to be received by the destination. A forced routing protocol is required to direct all data outside the jammed region and prevent resend requests and failed data delivery. Tracking and localization algorithms are divided into range-free and range-base. Range-free algorithms utilize the geometric node's position and the change of network topology to estimate jammer location. Centroid-based location (CL) and weighted centroid-based location (WCL) are examples of range-free location algorithms [4; 5]. Both techniques are sensitive to the sensor's location, the position of the jammer, and the density of nodes. The existing boundary node selection algorithms are concerned with detecting nodes surrounding holes or the edge of the network. Based on our knowledge, there has been no research conducted to detect boundary nodes during jamming attacks. [PERSON] [6] introduced the Distributed Boundary Recognition Algorithm (BNST RA) to locate boundary nodes distributed around the hole and the network border. The algorithm is based on four levels. First, the author implemented the Virtual Hexagonal Landmark (VHL) to choose Closure Nodes (CNs) found close to the hole and on the border of the network, becoming landmark nodes (LNs).