ADCROSS: Adaptive Data Collection from Road Surveilling Sensors
18 Mar 2014-IEEE Transactions on Intelligent Transportation Systems (IEEE)-Vol. 15, Iss: 5, pp 2049-2062
TL;DR: The concept of k-strip length coverage along the road is introduced, which ensures a better sensing coverage for the detection of moving vehicles compared with the conventional barrier coverage and full area coverage, in terms of the availability of sufficient information for statistical processing and the number of sensors required to be active.
Abstract: Wireless sensor networks have grown significant attentions among researchers for providing a flexible and low-cost framework to design an architecture for Intelligent Transport Systems. The inherent challenges in distribution and management of sensor networks along the road require an application-specific protocol support for the network connectivity, the sensing coverage, the reliable data forwarding, and the network lifetime improvement. This paper introduces the concept of k-strip length coverage along the road, which ensures a better sensing coverage for the detection of moving vehicles compared with the conventional barrier coverage and full area coverage, in terms of the availability of sufficient information for statistical processing and the number of sensors required to be active. To extend the network lifetime, every sensor follows a sleep–wakeup schedule maintaining the network connectivity and the k-strip length coverage. This scheduling problem is modeled as a graph optimization, the NP-hardness of which motivates to design a centralized heuristic, providing an approximate solution. As a sensor network is inherently distributed in nature, properties of the centralized heuristic are explored to design a per-node solution based on local information. Performance of the proposed scheme is analyzed through simulation results.
Citations
More filters
TL;DR: This work proposes two vehicle detection algorithms based on a cross-correlation technique in wireless magnetic sensor networks for on-street parking detection and vehicle speed estimation (VSE).
Abstract: 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%.
50 citations
Cites background from "ADCROSS: Adaptive Data Collection f..."
...A natural candidate for the design of ITS is wireless sensor network (WSN), which has the ability of providing reliable, cost-effective, and scalable solutions [1]....
[...]
TL;DR: 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 and has a significant improvement in detection accuracy.
Abstract: 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.
15 citations
Cites background from "ADCROSS: Adaptive Data Collection f..."
...networks (WSNs) and update the status of parking space in real-time [4]....
[...]
TL;DR: A robust vehicle detection method, which combines the data feature of global positioning system (GPS) satellites’ signal-to-noise ratios (SNRs) and magnetic signals to achieve vehicle detection and has a strong practical significance for the battery-powered WVDs.
Abstract: The wireless vehicle detectors (WVDs) based on the magnetic sensor for outdoor parking spaces have been studied in many papers. However, the magnetic signal has a blind zone between the front and rear wheels of vehicles, thus it is difficult for the WVDs to judge whether the magnetic signal is affected by the vehicle on the current or adjacent parking space. This paper proposes a robust vehicle detection method, which combines the data feature of global positioning system (GPS) satellites’ signal-to-noise ratios (SNRs) and magnetic signals to achieve vehicle detection. Since the high energy consumption of GPS receivers, the reference background of GPS-based method is calculated in the wireless access point (WAP), and the WVDs spend no energy on the background calculation in the proposed method. Moreover, the GPS receiver is activated to assist in the vehicle detection, when the data feature of magnetic signals is insufficient for the WVDs to make an accurate decision on the vehicle status, which improves the vehicle detection accuracy and reduces the average energy consumption of WVDs. Experiments show that the vehicle detection accuracy of our proposed method is up to 99.83%, when the sampling rate of magnetic sensor is 1 Hz, and it has a strong practical significance for the battery-powered WVDs.
4 citations
Cites background from "ADCROSS: Adaptive Data Collection f..."
...the research of SPMS is getting more and more attention from the governments and researchers [2]....
[...]
TL;DR: In this paper , the state-of-the-art approaches most relevant to the traffic load cognition on road bridges, including in-site measurement and data-driven simulation, are introduced.
3 citations
01 Aug 2018
TL;DR: A D-S based collaborative decision making (DS-CDM) has been proposed to transfer the critical states to final states by using adjacent collaborative MSNs to deal with the interferences from moving vehicles.
Abstract: A high-accuracy solution for parking surveillance based on collaborative decision making by using magnetic sensor nodes (MSN s) has been proposed. The magnetic distortions induced by vehicles can be measured by MSNs which connect with each other though wireless sensor network. However, the main challenge lies in the difficult of eliminating interferences in the measurements caused by adjacent moving vehicles or parking vehicles. In order to achieve the goal of high-accuracy, our work focus attention on dealing with these interferences. Due to their feature of temporality, the interferences from moving vehicles can be filtered by an efficient state-machine. Therefore, a multi-interim finite-state machine (MiFSM) has been introduced to deal with the interferences from moving vehicles. Besides, because most of vehicles induce significant disturbance, they can be correctly detected by MiFSM without any further detection. However, a few complicated and uncertain situations induced by adjacent parking vehicles are difficult to detection relying on a single MSN. So, these situations are defined as critical states in MiFSM and need further decision. In view of the simplicity and effectiveness of D-S evidence theory in processing uncertain information, a D-S based collaborative decision making (DS-CDM) has been proposed to transfer the critical states to final states by using adjacent collaborative MSNs. Experimental verification shows that our solution has a significant improvement in detection accuracy, as about 99.8% for vehicle arrival and 99.9% for vehicle departure.
1 citations
Cites background from "ADCROSS: Adaptive Data Collection f..."
...Due to the ability of providing reliable, cost-effective and scalable solution, WSN becomes a main technology driver behind largescale SPS [6]....
[...]
...In SPS, most of the work-time of WSN is in a light traffic load....
[...]
...The smart parking system (SPS) is always a strategic solution for resolving this problem by providing the real-time parking occupancy information for drivers and administrators [2]....
[...]
...However, it is hard to satisfy the transmission requirement in the application of large-scale SPS without extending....
[...]
References
More filters
TL;DR: This paper proposes S-MAC, a medium access control (MAC) protocol designed for wireless sensor networks that enables low-duty-cycle operation in a multihop network and reveals fundamental tradeoffs on energy, latency and throughput.
Abstract: This paper proposes S-MAC, a medium access control (MAC) protocol designed for wireless sensor networks. Wireless sensor networks use battery-operated computing and sensing devices. A network of these devices will collaborate for a common application such as environmental monitoring. We expect sensor networks to be deployed in an ad hoc fashion, with nodes remaining largely inactive for long time, but becoming suddenly active when something is detected. These characteristics of sensor networks and applications motivate a MAC that is different from traditional wireless MACs such as IEEE 802.11 in several ways: energy conservation and self-configuration are primary goals, while per-node fairness and latency are less important. S-MAC uses a few novel techniques to reduce energy consumption and support self-configuration. It enables low-duty-cycle operation in a multihop network. Nodes form virtual clusters based on common sleep schedules to reduce control overhead and enable traffic-adaptive wake-up. S-MAC uses in-channel signaling to avoid overhearing unnecessary traffic. Finally, S-MAC applies message passing to reduce contention latency for applications that require in-network data processing. The paper presents measurement results of S-MAC performance on a sample sensor node, the UC Berkeley Mote, and reveals fundamental tradeoffs on energy, latency and throughput. Results show that S-MAC obtains significant energy savings compared with an 802.11-like MAC without sleeping.
2,843 citations
01 Jul 2004
TL;DR: In this article, a medium access control scheme, called PEDAMACS, is proposed for a class of sensor networks with two special characteristics: the nodes periodically generate data for transfer to a distinguished node called the access point, and the nodes are (transmit) power and energy limited.
Abstract: We consider a class of sensor networks with two special characteristics. First, the nodes periodically generate data for transfer to a distinguished node called the access point. Second, the nodes are (transmit) power and energy limited, but the access point, which communicates with the 'outside world', is not so limited. Such networks might be used for instance when a geographically distributed physical process, such as traffic on a freeway or at an urban street intersection, is periodically sensed for purposes of process control. We propose a medium access control scheme, called PEDAMACS, for this special class of networks. PEDAMACS uses the high-powered access point to synchronize the nodes and to schedule their transmissions and receptions in a TDMA manner. The protocol first enables the access point to gather topology (connectivity) information. A scheduling algorithm then determines when each node should transmit its data, and the access point announces the transmission schedule to the other nodes. The scheduling algorithm ideally should minimize the delay-the time needed for data from all nodes to reach the access point. However, this optimization problem is NP-complete. PEDAMACS instead uses a polynomial-time scheduling algorithm which guarantees a delay proportional to the number of nodes in the sensor network. Because PEDAMACS schedules node transmissions, its performance is much better than that of protocols designed for more general contention (or random access) networks in terms of power consumption, delay, fairness, and congestion control. The comparison is based on simulations in TOSSIM, a simulation environment for TinyOS, the operating system for the Berkeley sensor nodes. For the traffic application we consider, the PEDAMACS network provides a lifetime of several years compared to several months and days based on random access schemes with and without sleep cycles respectively, making sensor network technology economically viable.
344 citations
TL;DR: For the traffic application the authors consider, the PEDAMACS network provides a lifetime of several years compared to several months and days based on random access schemes with and without sleep cycles, respectively, making sensor network technology economically viable.
Abstract: PEDAMACS is a Time Division Multiple Access (TDMA) scheme that extends the common single hop TDMA to a multihop sensor network, using a high-powered access point to synchronize the nodes and to schedule their transmissions and receptions. The protocol first enables the access point to gather topology (connectivity) information. A scheduling algorithm then determines when each node should transmit and receive data, and the access point announces the transmission schedule to the other nodes. The performance of PEDAMACS is compared to existing protocols based on simulations in TOSSIM, a simulation environment for TinyOS, the operating system for the Berkeley sensor nodes. For the traffic application we consider, the PEDAMACS network provides a lifetime of several years compared to several months and days based on random access schemes with and without sleep cycles, respectively, making sensor network technology economically viable.
289 citations
Journal Article•
TL;DR: A robust real-time vehicle detection algorithm for both sensors was developed, and the magnetic sensor was shown to be superior to the acoustic sensor, allowing the wireless sensor network system to be scalable and deployable everywhere in the traffic networks.
Abstract: This report describes the prototype design, development, analysis and performance of a traffic surveillance system that is based on wireless sensor networks. Vehicle classification and reidentification schemes for low-cost, low-power platforms with limited computation resources were developed and tested. Both acoustic and magnetic sensors were tested. A robust real-time vehicle detection algorithm for both sensors was developed, and the magnetic sensor was shown to be superior to the acoustic sensor. The detection accuracy was shown to be comparable to that of inductive loop detectors while also having a much higher configuration flexibility, thus allowing the wireless sensor network system to be scalable and deployable everywhere in the traffic networks.
186 citations
"ADCROSS: Adaptive Data Collection f..." refers background in this paper
...Low-cost sensor devices are deployed over the road, which sense different parameters of the moving vehicles such as vehicle speed, direction, and pressure....
[...]
TL;DR: This work describes a new shortest paths algorithm that achieves the same O(nm) worst-case time bound as Bellman-Ford algorithm but is superior in practice.
152 citations
"ADCROSS: Adaptive Data Collection f..." refers background in this paper
...This solves the first step of the optimal CSLC graph problem....
[...]