Recently, with the increasing difficulty of parking in cities, the wireless vehicle detectors (WVDs) based on Internet of Thing (IoT) technology and magnetic sensor have been widely used to obtain parking space status data, which lay the foundation for smart parking management system (SPMS). However, there is a weak magnetic region under every vehicle where the magnetic variation is even smaller than that distorted by adjacent vehicles, which leads to the accuracy decrease of the magnetometer-based vehicle detection algorithm. To improve the performance of the WVDs, an IoT-Driven vehicle detection method which combines the heterogeneous data feature of UWB channel with that of magnetic signal is proposed in this paper. In the proposed method, length of the propagation path and signature of the channel impulse response (CIR) with respect to the vehicles, which can be obtained from UWB modules, are introduced to achieve vehicle detection. Moreover, considering that the energy consumption of UWB modules is much higher than that of magnetic sensors, the UWB-based vehicle detection algorithm is only activated when the signature of magnetic signals cannot enable magnetometer-based vehicle detection algorithm to output an accurate decision. Finally, the proposed method was evaluated in a commercial parking lot under different conditions. The experiments show that it has a detection accuracy of 98.81% when the sampling rate of magnetic sensor is 1 Hz.

The street lighting, which is a ubiquitous utility in cities, plays an important role in improving comfort and safety of cities, as well as brings a heavy financial burden for governments to sustain its operation. Hence, the Intelligence Street Lighting system (ISLs) based on the Internet of Things (IoT) technology, committed to improving the quality of life of citizens by minimizing electrical energy consumption, has attracted the attention of the governments and researchers recently. To achieve energy saving, the approach of adjusting streetlights' illuminance according to the traffic parameters obtained by additional sensors, has been proposed in many existing works. However, adding new sensors will increase the cost of ISLs, thereby restricting its application and promotion. The paper proposes a novel traffic-adaptive street lighting scheme (TaSLC), whose traffic parameters are extracted from the signatures of received signal strengths (RSS) stem from the behaviors of vehicles and pedestrians moving on the roads, and dimming streetlights accordingly. Therefore, the proposed TaSLC can improve the performance of ISLs without any additional cost. Finally, the feasibility and performance of TaSLC have been evaluated in a real testbed, the experimental results show that the accuracy of TaSLC in detecting road users is up to 95% and the electric energy consumed by TaSLC is only 10.5% of that consumed by existing methods.

Wireless sensing-based human-vehicle recognition (HVR) methods have become a research hotspot due to their non-invasive, low-cost and ubiquitous advantages. However, existing methods usually have limited applicability and low recognition performance. Furthermore, the antenna deployment height and the carrier frequency of wireless signals are also key factors that affect HVR performance, but few works deeply explore their impact on HVR tasks. To address these issues, inspired by the powerful feature learning capability of recently-merged deep learning techniques, this paper proposes an attention-based convolutional neural network (CNN) model named wireless-based lightweight attention deep learning method (Wi-LADL) for HVR. Wi-LADL takes received signal strength (RSS) as input, since RSS contains the useful temporal feature of the gait for HVR. Wi-LADL consists of several lightweight CNN modules in series and a convolutional block attention mechanism (CBAM) to learn high-level features from RSS signals. Experimental results on the developed five-category RSS dataset show that the proposed Wi-LADL not only achieves higher accuracy, but also exhibits lower computational complexity compared with state-of-the-art HVR methods. In particular, Wi-LADL obtains the best performance with an average accuracy of 98.8% at 2.4 GHz band and an antenna height of 0.8 m on HVR tasks. Specifically, when classifying one-pedestrian, two-pedestrians, one-bicycle, two-bicycles and one-car, the proposed method achieves recognition accuracy of 97.06%, 97.14%, 100%, 100% and 100%, respectively. To facilitate the development of wireless sensing technology, the developed new RSS dataset has been publicly available at https://github.com/TZ-mx/WiParam.

In the realm of traffic parameter measurement, wireless sensing-based human-vehicle recognition methods have been pivotal due to their low cost and non-invasive nature. Traditionally, these methods have relied on the 2.4 GHz frequency band, often neglecting the rich potential of the sub-GHz bands. Furthermore, the energy attributes of wireless signals are influenced by both antenna height and carrier frequency, yet few studies have explored their impact on human-vehicle recognition performance. Addressing this critical research gap, this study introduces an innovative convolutional neural network-based method that leverages both sub-GHz bands and variable antenna heights. Specifically, this paper focuses on two key aspects: received signal strength signal-to-noise ratio analysis and wireless sensing-based human-vehicle recognition performance analysis. Experimental results demonstrate that the optimal human-vehicle recognition performance is achieved with 2.4 GHz wireless signals and an antenna height of 0.8 m, resulting in an average vehicle recognition accuracy of 95.96%. Besides, the dataset with various carrier frequencies and antenna heights has been publicly available at https://github.com/TZ-mx/mixed-RSS-dataset .

Wireless-sensing-based human–vehicle classification (WHVC) offer cost-effective advantages and enhance the detection efficiency of traffic parameters in intelligent transportation systems (ITSs). Existing WHVC methods primarily utilize channel state information (CSI) or received signal strength (RSS) features extracted from the surrounding wireless signals. Although CSI data provides more detailed and accurate channel information compared to RSS data, extracting and processing CSI is more challenging than RSS. Moreover, for applications that do not require fine-grained human–vehicle classification, such as intelligent street lighting systems, RSS-based WHVC has the advantages of easy implementation and low cost. Therefore, investigating the performance of CSI- and RSS-based WHVC methods in different application scenarios could provide valuable insights for the WHVC domain. To address this issue, this article proposes a deep-learning-based WHVC method, which employs deep learning as a tool to evaluate the performance of RSS and CSI methods in various classification tasks. Specifically, this article collects CSI and RSS data for seven different classification tasks in real traffic road scenarios and evaluates these tasks using a convolutional neural network (CNN)-based deep learning model designed in this article. Experimental results demonstrate that for road user categories less than four, RSS-based WHVC achieves higher accuracy than CSI-based WHVC. However, as the number of categories increases, CSI-based WHVC exhibits superior accuracy compared to RSS-based WHVC. Additionally, the developed data set is publicly available at https://github.com/TZ-mx/mixed-dataset .