## 2 Related Work  
Various sensors have been considered for the acquisition of traffic information in ITS. Existing systems can be considered as either intrusive or non-intrusive, depending on the sensor position. Intrusive sensors are installed under or across the pavement. They provide accurate traffic information, but the installation and maintenance of these sensors can cause traffic disruption. Examples of intrusive sensors include inductive loop detectors, pneumatic tubes, piezoelectric sensors and weigh-in-motion [1; 2; 3; 4]. Non-intrusive sensors are installed above or to the side of the road, ensuring minimal disruption to traffic flow. Such sensors may utilize microwave radar, infrared, video, ultrasonic systems and acoustic sensors [5; 6; 7; 8; 9]. Existing systems collect highly accurate traffic information, but require high computing power, wired power supplies and wired communication. This increases their cost and decreases scalability.  
To address these problems, traffic information acquisition systems based on WSNs have been studied. WSN-based systems use a wireless network and are battery driven, which enhances the system scalability and reduces the cost. In [10], a WSN-based vehicle detection system was introduced that used small magnetic sensors to replace conventional inductive loop detectors. Traffic flow and speed information could be collected with high accuracy, but this system did not allow for vehicle classification. Accurate vehicle classification was achieved by applying both magnetic and accelerometer sensors [11], where each vehicle could be detected by magnetic sensors and classified by axle detection using accelerometer sensors. A detection system has been proposed that uses acoustic sensors to provide wide-range, low-cost traffic surveillance [12]. This system collects speed and flow data at a master node, and traffic congestion is detected by the sensor nodes. Pyro-electric sensors were employed to detect vehicles in a parking space and a typical road environment [13,14]. In [13], pyro-electric sensors were used to sense the status of car parking space. The status information is displayed for the users on the LED (Light-Emitting Diode) screen. The authors in [14] tried to increase vehicle detection accuracy by combining different type of sensors, such as acoustic, magnetic, accelerometer and pyro-electric sensors.  
The WSN-based systems mentioned above have proved that low-cost, highly scalable traffic surveillance can be accomplished. However, many issues remain to be overcome. The approaches in [10,11] showed that vehicle detection and classification can be achieved using multimodal sensor data, but these systems should be installed on the pavement, which will increase their cost and cause traffic disruption. The approach reported in [12] is non-intrusive, but could not achieve high detection accuracy across multiple lanes. The studies in [13,14] are also non-intrusive, but did not provide detailed methods of power saving and lightweight detection algorithm.  
In our previous paper, we introduced entire architecture of vehicle detection systems with ultrasonic sensors in WSN [15]. However, the detailed methodologies of power saving and lightweight detection algorithm were not provided. Thus, in this paper, we propose advanced methods of power saving and lightweight detection algorithm using WSN-based ultrasonic sensors. Figure 1 illustrates some typical mount positions for ultrasonic sensors. We use the horizontal mount position shown in Figure 1c, as this enhances the scalability of the proposed system.