A WSN-Based Tool for Urban and Industrial Fire-Fighting

Clemente, Alberto de San Bernabé; Dios, J.R. Martínez-de; Baturone, Aníbal Ollero

doi:10.3390/s121115009

Cited by 18 publications

(6 citation statements)

References 27 publications

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“…Conventional fire-fighting can also be improved, as shown in [83]. This system consists of a TelosB based wireless sensor network, where the nodes are placed on firefighters' bodies.…”

Section: Applicationsmentioning

confidence: 99%

The Role of Advanced Sensing in Smart Cities

Hancke

Silva

2012

Sensors

510

271

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In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities.

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“…Conventional fire-fighting can also be improved, as shown in [83]. This system consists of a TelosB based wireless sensor network, where the nodes are placed on firefighters' bodies.…”

Section: Applicationsmentioning

confidence: 99%

The Role of Advanced Sensing in Smart Cities

Hancke

Silva

2012

Sensors

510

271

View full text Add to dashboard Cite

show abstract

“…They require accurate RSSI maps of the environment, which should be re-calculated if the setting changes. In [29] each node uses the location of surrounding nodes to train its RSSI-range model. However, the method cannot capture the interactions with the local environment surrounding the target.…”

Section: Related Workmentioning

confidence: 99%

“…11. Cumulative tracking error obtained by RSSI-only tracking using: the default RSSI-range model, the RSSI calibration method in [29] and the training method proposed in Section 5 . models and once trained, the sensor selection method tends to deactivate cameras due to their higher energy consumption.…”

Section: Proof Of Conceptmentioning

confidence: 99%

“…First, we evaluate the accuracy of the training RSSI-range model method proposed in Section 5 in a set of experiments with 20 random settings and robot trajectories. We performed the experiments, logged all RSSI measurements and off-line executed the EIF that integrates the RSSI measurements using three different RSSI-range models: the default RSSI-range model, the RSSI calibration method presented in [29] and the RSSI-range model training method proposed in this paper. Fig.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Efficient integration of RSSI for tracking using Wireless Camera Networks

2017

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This paper proposes a scheme that efficiently exploits synergies between RSSI and camera measurements in cluster-based target tracking using Wireless Camera Networks (WCNs). The scheme is based on the combination of two main components: a training method that accurately trains RSSI-range models adapted to the conditions of the particular local environment; and a sensor activation/deactivation method that decides on the individual activation of sensors balancing the different information contributions and energy consumptions of camera and RSSI measurements involved in sensing. The scheme also includes a distributed Extended Information Filter that integrates all available measurements. The combination of these components originates self-regulated behaviors that drastically reduce power consumption and computational effort with no significant tracking degradation w.r.t. existing schemes based exclusively on cameras. Besides, it shows better robustness to target occlusions. The proposed scheme has been implemented and validated in real experiments.

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“…The application is to monitor the status of the event at the base station [ 23 ]. Having accurate estimations of the locations of the robot and of each node is necessary for accurate event monitoring and also enables advanced robot-sensor network cooperation strategies of interest in these problems, such as using the robot for sensor node deployment [ 24 ], replacement [ 25 , 26 ] or collecting data from the sensors [ 27 ], among others.…”

Section: General Overviewmentioning

confidence: 99%

An Adaptive Scheme for Robot Localization and Mapping with Dynamically Configurable Inter-Beacon Range Measurements

Torres-González

Dios

Ollero

2014

Sensors

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This work is motivated by robot-sensor network cooperation techniques where sensor nodes (beacons) are used as landmarks for range-only (RO) simultaneous localization and mapping (SLAM). This paper presents a RO-SLAM scheme that actuates over the measurement gathering process using mechanisms that dynamically modify the rate and variety of measurements that are integrated in the SLAM filter. It includes a measurement gathering module that can be configured to collect direct robot-beacon and inter-beacon measurements with different inter-beacon depth levels and at different rates. It also includes a supervision module that monitors the SLAM performance and dynamically selects the measurement gathering configuration balancing SLAM accuracy and resource consumption. The proposed scheme has been applied to an extended Kalman filter SLAM with auxiliary particle filters for beacon initialization (PF-EKF SLAM) and validated with experiments performed in the CONET Integrated Testbed. It achieved lower map and robot errors (34% and 14%, respectively) than traditional methods with a lower computational burden (16%) and similar beacon energy consumption.

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A WSN-Based Tool for Urban and Industrial Fire-Fighting

Cited by 18 publications

References 27 publications

The Role of Advanced Sensing in Smart Cities

The Role of Advanced Sensing in Smart Cities

Efficient integration of RSSI for tracking using Wireless Camera Networks

An Adaptive Scheme for Robot Localization and Mapping with Dynamically Configurable Inter-Beacon Range Measurements

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