Wireless Magnetic Sensor Networks Research Articles

Collaborative Sensing-Based Parking Tracking System With Wireless Magnetic Sensor Network

Wireless magnetic sensor networks provide a low-cost technology for the outdoor real-time parking tracking system. However, the magnetic sensor is susceptible to the interference caused by vehicles parked in adjacent spaces. Most existing detection systems are solely based on local sampled data from the sensor node and they are prone to high false and missed detection rates in the case of a low signal-to-noise ratio. This paper proposes a collaborative sensing parking tracking system by fusing the signals of the adjacent sensors to eliminate the interference. Sensor nodes convert magnetic signals into signatures represented by peak values, and then the base station computes the spatial relationships between adjacent signatures to identify parking event or interference. An experiment system includes sixty sensor nodes that were deployed in bay parking spaces. Experiment results verified that the proposed system has better detection accuracy than existing systems.

An Antiinterference Traffic Speed Estimation System With Wireless Magnetic Sensor Network

Wireless magnetic sensor networks (WMSNs) provide a low-power, low-cost solution for traffic information acquisition. Systems used for traffic speed estimation based on WMSNs have been well studied only when the geomagnetic field has not interfered. However, the widely developed electric rail transit systems, such as subways, light rails, and high-speed rail systems, produce magnetic field radiation that interferes with the geomagnetic field. So it is imperative to filter out noise in order to increase the detection accuracy. This article proposes a system for traffic speed estimation which can effectively eliminate the geomagnetic background interference. A morphological filter is designed for removing the interference and extracting the magnetic signatures of vehicles. The speed estimation is implemented based on the signatures reidentification. Experimental results verify that the proposed system is more accurate than existing systems.

A Robust Parking Detection Algorithm Against Electric Railway Magnetic Field Interference

Wireless magnetic sensor networks (WMSNs) offer an effective and efficient low-cost alternative for real-time parking space monitoring. Current algorithms used for parking space detection based on the WMSNs have been well studied but not without eliminating the geomagnetic field interference. Electric railway systems, such as subways and light rail systems, produce magnetic field radiation that interferes with the geomagnetic field. The electric railway magnetic field interference has increased and is prominent in urban settings; therefore, it is imperative to filter out the interference in order to increase the detection accuracy. This paper proposes an algorithm for parking space detection, which can effectively eliminate the geomagnetic background interference. We solve the parking detection problem through signal filtering technology. Based on mathematical morphology, we designed two filters for extracting morphological structures from interfered magnetic signals. The experimental results verify that the proposed algorithm is more accurate than the existing algorithms.

Parking Detection Method Based on Finite-State Machine and Collaborative Decision-Making

A high-accurate parking detection method for wireless magnetic sensor networks is proposed in this paper, which is based on the combination of finite-state machine and collaborative decision-making. The magnetic disturbance induced by a vehicle can be sensed and processed to obtain the availability of a parking space by a magnetic sensor. However, the main challenge lies in the difficulty of eliminating the interferences from adjacent vehicles that decrease the accuracy. The vehicles include moving vehicles on adjacent roads and parking vehicles in adjacent parking spaces. For simplicity and low-energy consumption, a multi-interim finite-state machine (MiFSM) is proposed to deal with the interferences from moving vehicles. Our method contains preliminary detection and final detection. Using MiFSM, most of the parking vehicles can be correctly detected in the preliminary detection. However, the adjacent parking vehicles may cause more complicated interferences. It is hard to distinguish these interferences from the disturbances induced by “weak-magnetic” vehicles above on the detecting sensor. The “weak-magnetic” vehicles cause small magnetic disturbance because of their high chassis or short car-body. Therefore, by using the collaborative information of adjacent sensors, a Dempster–Shafer evidence theory-based collaborative decision-making is developed to cope with these complicated interferences in the final detection. The experimental results show that our work has a significant improvement in detection accuracy, as about 99.8% for vehicle arrival and 99.9% for vehicle departure. The proposed method can also be extended for moving vehicle detection, speed estimation, and vehicle classification in the applications of intelligent traffic system.

A Vehicle Speed Estimation Algorithm Based on Dynamic Time Warping Approach

At present, the research of vehicle speed estimation algorithm based on wireless magnetic sensor networks has made great progress. The general idea of the existing algorithms is to calculate the similarity of two magnetic signatures by Euclidean distance or cross correlation function. However, these methods are not well suitable for the time distorted waveforms. This paper adopts the dynamic time warping (DTW) approach to measure the similarity, and then a speedup version of DTW is proposed. Experimental results are presented from a large number of vehicles to show that the proposed speedup DTW reduces the runtime and improves the accuracy of speed estimation.

Wireless Magnetic Sensor Network for Road Traffic Monitoring and Vehicle Classification

Abstract Efficiency of transportation of people and goods is playing a vital role in economic growth. A key component for enabling effective planning of transportation networks is the deployment and operation of autonomous monitoring and traffic analysis tools. For that reason, such systems have been developed to register and classify road traffic usage. In this paper, we propose a novel system for road traffic monitoring and classification based on highly energy efficient wireless magnetic sensor networks. We develop novel algorithms for vehicle speed and length estimation and vehicle classification that use multiple magnetic sensors. We also demonstrate that, using such a low-cost system with simplified installation and maintenance compared to current solutions, it is possible to achieve highly accurate estimation and a high rate of positive vehicle classification.

A Cross-Correlation Technique for Vehicle Detections in Wireless Magnetic Sensor Network

Vehicle detections are an important research field and attract many researchers. Most research efforts have been focused on vehicle parking detection (VPD) in indoor parking lot. For on-street parking, strong noise disturbances affect detection accuracy. To deal with vehicle detections in on-street environment, we propose two vehicle detection algorithms based on a cross-correlation technique in wireless magnetic sensor networks. One is for VPD, and the other one is for vehicle speed estimation (VSE). The proposed VPD algorithm combines the state-machine detection and the cross-correlation detection. In the VSE, speed estimation is based on the calculation of the normalized cross correlation between the signals of two sensors along the road with a certain spacing. Experimental results show that the VPD has an accuracy of 99.65% for arrival and 99.44% for departure, while the VSE has an accuracy of 92%.

Real-Time Measurement of Link Vehicle Count and Travel Time in a Road Network

A system is described that measures the vehicle count and travel time in the links of a road network. The measurements require matching vehicle signatures recorded by a wireless magnetic sensor network. The matching algorithm is based on a statistical model of the signatures. The model itself is estimated from the data. The approach is first discussed for a single-lane road and extended to multiple-lane roads. The algorithm yields a correct matching rate of 75% for a false matching rate of 5% and reliably estimates the number of vehicles on each link and its travel-time distribution. The system is tested on a 0.9-mi-long segment of San Pablo Avenue, Albany, CA.

Traffic Measurement and Vehicle Classification with Single Magnetic Sensor

Wireless magnetic sensor networks offer an attractive, low-cost alternative to inductive loops for traffic measurement in freeways and at intersections. In addition to providing vehicle count, occupancy, and speed, these sensors yield information (such as non-axle-based vehicle classification) that cannot be obtained from standard loop data. Because such networks can be deployed quickly, they can be used (and reused) for temporary traffic measurement. This paper reports the detection capabilities of magnetic sensors on the basis of two field experiments. The first experiment collected a 2-h trace of measurements on Hearst Avenue in Berkeley, California. The vehicle detection rate was better than 99% (100% for vehicles other than motorcycles), and estimates of average vehicle length and speed appear to have been better than 90%. The measurements also yield intervehicle spacing or headways, revealing interesting phenomena such as platoon formation downstream of a traffic signal. Results of the second experiment are preliminary. Sensor data from 37 passing vehicles at the same site are processed and classified into six types. Sixty percent of the vehicles are classified correctly when length is not used as a feature. The classification algorithm can be implemented in real time by the sensor node itself, in contrast to other methods based on high-scan-rate inductive loop signals, which require extensive off-line computation. It is believed that if length were used as a feature, 80% to 90% of vehicles would be correctly classified.

Traffic Measurement and Vehicle Classification with Single Magnetic Sensor

Wireless magnetic sensor networks offer an attractive, low-cost alternative to inductive loops for traffic measurement in freeways and at intersections. In addition to providing vehicle count, occupancy, and speed, these sensors yield information (such as non-axle-based vehicle classification) that cannot be obtained from standard loop data. Because such networks can be deployed quickly, they can be used (and reused) for temporary traffic measurement. This paper reports the detection capabilities of magnetic sensors on the basis of two field experiments. The first experiment collected a 2-h trace of measurements on Hearst Avenue in Berkeley, California. The vehicle detection rate was better than 99% (100% for vehicles other than motorcycles), and estimates of average vehicle length and speed appear to have been better than 90%. The measurements also yield intervehicle spacing or headways, revealing interesting phenomena such as platoon formation downstream of a traffic signal. Results of the second experiment are preliminary. Sensor data from 37 passing vehicles at the same site are processed and classified into six types. Sixty percent of the vehicles are classified correctly when length is not used as a feature. The classification algorithm can be implemented in real time by the sensor node itself, in contrast to other methods based on high-scan-rate inductive loop signals, which require extensive off-line computation. It is believed that if length were used as a feature, 80% to 90% of vehicles would be correctly classified.