# Special Issue on Advanced Wireless Sensor Networks for Emerging Applications
###### Abstract
A traffic surveillance and speed violation detection system can be established with high precision by deploying the collaborative WSNs, instead of using a traffic enforcement vehicle [20]. Additionally, the optimal path for a specific destination can be extracted based on the monitoring of small- to medium-sized road networks. In [21], the WSN is deployed to the smart parking system to provide the detection of vehicles, reservation of parking spaces, and guidance to external systems. In an autonomous vehicle, the light detection and ranging (LiDAR) sensor plays a critical role because it can provide the perception of all light circumstances. The vehicle-to-vehicle communication, LiDAR system, and GPS are used to construct sensor fusion algorithms in connected-vehicles applications for more accurate localization and object detection. In order to extract dense features efficiently in the large raw data sets collected by LiDARs, a novel framework for point cloud registration is developed in [22], which uses the graph attention network as an attention mechanism enriching the relationships between point clouds. The data can be interchanged between roadside and vehicles or between vehicles to prevent car accident and control road network, which can be translated as the mechanism of the WSN. Machine learning (ML) techniques are being applied for more advanced WSNs [23]. The use of ML techniques is beneficial for enhanced edge computing at sensor devices. In the IoT systems, the ML technologies are mainly used for node localization, coverage and connectivity problems, medium access control (MAC) and routing protocols, data collection, fault detection, energy harvesting, and monitoring system. For node localization, which is significant for the path planning of mobile sensor nodes, some ML algorithms, such as the support vector machine, k-nearest neighbor algorithm, reinforcement learning (RL)-based algorithms [24; 25], are applied to identify the current location of a sensor node [26]. The RL algorithm particularly corresponds to the deep learning (DL) algorithm. In [27], a multi-hop ad hoc IoT network is constructed to determine the transmission ranges of wireless sensors based on the RL approach for appropriate connectivity between sensors and maximization of the lifetime of the WSN. A sensor device is used as a decision-making agent deciding the transmission range, which maximizes the network throughput and minimizes the transmission energy consumption. An energy efficient routing protocol based on the RL is proposed to maintain a stable WSN by distributing residual energy of sensor nodes more evenly [28]. The reward of the RL, utilized to select appropriate forwarders of packets, is calculated based on the residual energy level of each sensor node as well as the energy distribution in a group of sensor nodes. In [29], a combined prediction model, which combines a back propagation neural network with a genetic algorithm and particle swarm optimization, respectively, is used for wind farm energy harvesting based on the weather data gathered by the WSN. Additionally, for efficient data transmission of PV and wind power production data predicted by kernel recursive least-square (KRLS) algorithm, a link scheduling method based on sensor node attributes is presented in [30].