Jiangchen Li

An Energy-Efficient Vehicle Detection Algorithm for a Complex Urban Traffic Environment

Magnetometer-based sensors have been proven to be an effective method for vehicle detection in intelligent traffic systems (ITS). Many algorithms have been proposed to improve the performance of magnetometer-based traffic sensing. However, few studies consider the effectiveness of these algorithms in context with a high-density traffic flow, which is typical in urban areas of developing countries such as China. In addition, the energy-efficiency of the existing algorithms has been largely ignored, hence limiting the application of the battery-powered sensor network in practice. Considering both high-density traffic flow and energy-efficiency, this paper presents a real-time vehicle detection algorithm using magnetometers. The proposed algorithm first assesses the data after the noise removal, and extracts a set of magnetic features from these data. The extracted features are then fed into a finite state machine with self-adaptive parameters. The proposed algorithm has been implemented in an embedded processor manufactured by Texas Instruments, and a wireless sensor network carrying sensor nodes running the authors proposed algorithms is deployed as the test bed in several major roads inside and outside the Huazhong University of Science and Technology campus. Tested by 6 traffic flow datasets collected during rush-hour, the results show that the authors algorithm can achieve 91% or above detection accuracy under complex urban traffic environment.

A new secure method for the transmission of protection information between substations based on compressed sensing

Wide-Area protection uses the global information to detect faults and to achieve stability control, which is a hot research topic nowadays. However, wide-area protection system needs to use information from a number of substations. Therefore, communications reliability requirement is high. To ensure reliability of the information transmitted between substations, this paper presents an encoding method based on compressed sensing (CS). This method creates sparsity for the data to be transferred and maps the information to the frequency domain before transmission. At the receiving end, an orthogonal matching pursuit (OMP) reconstruction algorithm is used to reconstruct the original information. The simulation results show that even with the worst case bit change, or when the communication channel has noise, the normalized root mean square error (NMRSE) is still acceptable. The method simplifies the encoding and decoding complexity, greatly improves efficiency.

Wide Area Power System Fault Detection Using Compressed Sensing to Reduce the WAN Data Traffic

With the increasingly complex power system, wide area protection, using global data obtained from different substations through communications, has been a hot research topic for some time. However, the overall transmission of large amounts of data will cause communication network congestion, which will lead to delay and loss of data. Therefore building an algorithm which can make use of a reduced number of global data to identify the fault area is very useful. This paper proposes a down-sampling matrix to reduce the original data. For example, a protection system requiring 240 feature points of voltage data, if using the down-sampling matrix, will need only a minimum of 24 points, and still has a high probability to identify the fault zone. Simulation results show that when the data size M > 0.3, the result of classifying adjacent bus fault point is credible (greater than 60%), and when the data size M > 0.05, the result of classifying the non-adjacent bus fault point is credible (greater than 72%).

An energy-efficient sub-Nyquist sampling method based on compressed sensing in wireless sensor network for vehicle detection

The wireless magnetic sensor network is scalable and deployable for traffic surveillance. But active magnetic sensors of the wireless sensor node have high energy consumption which cannot be ignored. It is necessary to save energy of the wireless magnetic sensor node for vehicle detection. In this paper, based on compressed sensing (CS) by random down sampling matrix, an energy-efficient sub-Nyquist sampling method in magnetic sensor network is proposed for vehicle detection. With this new sampling method, the active magnetic sensors average frequency is less than the Nyquist standard sampling frequency, which reduces the energy consumption of the active sensor, while extending the lifetime of the wireless sensor nodes. When the Compressed Radio (CR) meets the maximum value of 60%, the new sampling method doubles the wireless magnetic sensor nodes lifetime and maintains vehicle detection accuracy.

Signal Priority Request Delay Modeling and Mitigation for Emergency Vehicles in Connected Vehicle Environment

Connected vehicle-based signal priority control is widely regarded as an advanced method for improving travel efficiency of an emergency vehicle when passing through intersections. However, the vehicle-to-everything (V2X) communication delay is a critical factor affecting the performance of signal request and has rarely been considered in existing studies. This paper conducted a comprehensive delay analysis of the preemption signal request and its influence on practical preemption control for the emergency vehicle. First of all, a general end-to-end delay decomposition model is formulated to analyze significant delay uncertainties from different sources. Then, a compensated distance strategy is adopted for cooperative preemption control to ensure the reliability of preemption control and minimize impacts on performance caused by communication delay. Based on the analysis of field data and numerical results, the proposed model is able to reveal characteristics of communication delay for multimodal traffic signal control with priority. The proposed communication delay compensation strategy shows clear benefits in improving the performance of signal preemption control priority for an emergency vehicle at intersections and therefore has potential to enhance V2X applications in a connected vehicle environment.

Dynamic traffic shockwave speed estimation in connected vehicle environment

Traffic state estimation is important for active traffic planning, management, and control. By utilizing traffic status over time and space, a key theoretical analysis tool, i.e., shockwave theory, can be adopted as efficient tools to solve bottleneck or congestion problems. Two methods, who are fixed point observations and mobile probe vehicle observations, are typical methods and popular in traffic analytical agencies and projects. But both of them has their problems and new insights are needed for current proactive and online traffic implementations. In this paper, a dynamic traffic shockwave estimation method with both high temporal resolution and high spatial resolution is proposed based on integrating vehicle detecting system data from fixed loop detectors and trajectory data from probe vehicles in a connected vehicle environment. The proposed method was validated in a prevailing dataset. Results showed that the proposed method combined the advantages of mobile and fixed data. As a result, the proposed method is effective in improving the spatial and temporal resolution of the shockwave estimation in the considered general scenario.

A practical method for validating data quality by RSE connectedness: A case study in China

Traffic safety has been one of the most primary social problems puzzling big cities over the world. It is widely believed that connected vehicle technology will tackle this problem. To achieve that, we installed RSE (Road Side Equipment) in urban road environment to communicate with OBE (On Board Equipment) through DSRC (dedicated short-range communication) to organize a DSRC network system. However, as a fundamental communication device, RSE may lose connections, which will have impacts on the quality of collected data and finally this might lead to failures of traffic applications. Therefore, it is necessary to validate the connection status of DSRC network environment. In this paper, based on analyzing non-continuous DSRC network system, a field test method for connected vehicle technology (CVT) application is proposed to verify whether such a network system is in good condition to ensure normal usages of traffic applications or not. Then, a case study in the field is conducted to evaluate the validities of some important parameters (i.e., latency, distance, and speed). We draw a conclusion that the network system was available for CVT application; but was desired to be improved.

Vehicle-to-Pedestrian Communication Modeling and Collision Avoiding Method in Connected Vehicle Environment

A generalized framework consisting of a stochastic model, a responsive control method, and typical scenarios using vehicle-to-pedestrian (V2P) communication is proposed to improve pedestrian safety. The proposed stochastic model formulates various effects of uncertainties in a V2P communication system. The responsive control method is used to improve pedestrian safety under V2P communication conditions. Results show that adopting only Bluetooth or Wi-Fi (wireless local area network) technology, which has a high establishment time, is not sufficient for V2P communication, whereas dedicated short-range communication (DSRC) featuring low latencies meets requirements. A case study in the field was conducted to evaluate the feasibility of responsive control; it was concluded that with the use of the proposed model and the responsive control method, Bluetooth technology combined with DSRC could be workable for active pedestrian protection.

pSense: An efficient and practical analog-to-information platform using COTS devices

A challenge in wireless sensing is to reduce power consumption during data transmission. Various methods aim at compressing amounts of data after sampling, which require additional computation energy or hardware. In this paper, pSense, a low complexity commercial off-the-shelf based (COTS-based) analog-to-information converter (AIC) platform is presented to minimize power consumption. A periodical pseudo-random down-sampling algorithm (PPRS) is employed to reduce energy consumption by controlling sampling numbers and features high scalability. Results show pSense can reconstruct signals within acceptable low error levels and having low energy consumptions.

An Effective Topology Optimization Algorithm for Wireless Network with Parking Surveillance Sensors

Magnetron-based wireless sensor networks (WSNs) have been widely applied for the parking space surveillance. In this paper, on the basis of genetic algorithm (GA), the authors will propose a solution for the network topology optimization based on a 2-tier tree-type WSN, which is an effective way of improving the overall performance of the WSN concerning parking surveillance. The proposed algorithm aims to assess the optimal placement and the quantity of the network access points (APs) when given the locations of the sensor nodes. The problem is formulated as an integer programming (IP) problem and it can be solved efficiently by using genetic algorithm (GA). Tested by a real WSN in a parking lot with about 200 parking spaces, the results illustrate that a placement of APs generated by the proposed algorithm is more effective, compared with one produced by an experienced engineer in a parking surveillance industry.