## 1 Introduction
Alternatively, learn from previous routing decisions and adapt to the traffic importance of information transmission to cope with unpredictable topology changes and challenges of energy constraints [11]. Node clustering and data aggregation In WSNs constrained by energy resources, it is ineffective to transmit all data directly to the receiver. A practical solution is to pass the data to a local aggregator (called a cluster head) that aggregates the data from all the sensors in its cluster and transmits it to the receiver, often saving energy for the nodes. How to select the best node as the cluster head among local sensors is always a trending research topic. In addition to the famous LEACH clustering mechanism, the CHEF cluster head election mechanism of fuzzy logic is also used to reduce the collection and calculation overhead and extend the life of the sensor network. Recently, some researchers proposed a clustering protocol based on a support vector, which can effectively allocate sensor nodes to the nearest cluster using machine learning methods, reduce energy consumption, and make better use of resources [12]. Cluster head selection methods combined with machine learning algorithms can reduce energy consumption and enhance the network life cycle. A role-free clustering algorithm based on the Q-learning algorithm is proposed to make each node have the ability to act as a cluster head node by combining the Q-learning algorithm with some dynamic network parameters [13]. Event detection and query processingEvent detection and query processing in WSNs is considered a functional requirement for any large-scale sensor network. Monitoring content in WSNs can be divided into three categories: event-driven, continuous-driven, or query-driven. How to design effective event detection and query processing solutions has been the focus of many researchers in WSNs. The most straightforward technique is to provide strict thresholds for perceived phenomena and alert system administrators to any violations. However, in the recent use of WSNs, event and query processing units are often complex and require more than predetermined thresholds. In this regard, many researchers have made improvements and proposed their algorithms and solutions. For example, a new event detection algorithm is proposed in the wireless network where sensor nodes are randomly deployed in space [14]. In an environment with strict execution time requirements, a WSNs data flow analysis framework based on deep learning is proposed [15], which can obtain reasonable and accurate query analysis results within the deadline. In recent years, the appearance of wireless network physical systems needs to support the real-time query of the physical environment through wireless sensor networks. To address this requirement, a real-time query scheduling algorithm (RTQS) is proposed in [16], which is a new conflict-free transmission scheduling method for real-time queries in wireless sensor networks. Localization and object positioning  
Localization is the process of determining the geographic coordinates of network nodes and components. Considering that the operation of most sensor networks is usually based on location, the position perception of sensor nodes is an essential function [17]. While it is possible to achieve position awareness of sensor nodes by using Global Positioning System (GPS) hardware in each node, this approach is not economically feasible in most large systems. In addition, GPS services may not be available in some observable environments. Relative position measurement is sufficient in some scenarios. However, the position of the sensor node can be sensed using the absolute position of the node because the relative position can be converted to the absolute position [18]. To enhance the performance of proximity-based positioning, additional measurements depending on distance, angle, or a combination of them can be used. Distance measurements can be obtained by utilizing various techniques such as RSSI, TOA, and TDOA.