Christian G. Claudel

Lessons learned on solar powered wireless sensor network deployments in urban, desert environments

The successful deployment of a large scale solar powered wireless sensor network in an urban, desert environment is a very complex task. Specific cities of such environments cause a variety of operational problems, ranging from hardware faults to operational challenges, for instance due to the high variability of solar energy availability. Even a seemingly functional sensor network created in the lab does not guarantee reliable long term operation, which is absolutely necessary given the cost and difficulty of accessing sensor nodes in urban environments. As part of a larger traffic flow wireless sensor network project, we conducted several deployments in the last two years to evaluate the long-term performance of solar-powered urban wireless sensor networks in a desert area. In this article, we share our experiences in all domains of sensor network operations, from the conception of hardware to post-deployment analysis, including operational constraints that directly impact the software that can be run. We illustrate these experiences using numerous experimental results, and present multiple unexpected operational problems as well as some possible solutions to address them. We also show that current technology is far from meeting all operational constraints for these demanding applications, in which sensor networks are to operate for years to become economically appealing.

UD-WCMA

Energy estimation and forecast represents an important role for energy management in solar-powered wireless sensor networks (WSNs). In general, the energy in such networks is managed over a finite time horizon in the future based on input solar power forecasts to enable continuous operation of the WSNs and achieve the sensing objectives while ensuring that no node runs out of energy. In this article, we propose a dynamic version of the weather conditioned moving average technique (UD-WCMA) to estimate and predict the variations of the solar power in a wireless sensor network. The presented approach combines the information from the real-time measurement data and a set of stored profiles representing the energy patterns in the WSNs location to update the prediction model. The UD-WCMA scheme is based on adaptive weighting parameters depending on the weather changes which makes it flexible compared to the existing estimation schemes without any precalibration. A performance analysis has been performed considering real irradiance profiles to assess the UD-WCMA prediction accuracy. Comparative numerical tests to standard forecasting schemes (EWMA, WCMA, and Pro-Energy) shows the outperformance of the new algorithm. The experimental validation has proven the interesting features of the UD-WCMA in real time low power sensor nodes. Introducing a new dynamical version of the (WCMA) forecast scheme.Proposing a new dynamical forecast scheme with adaptive parameters.The new proposed scheme adapts to the current day dynamics with minimal training.The new proposed scheme is validated using real data (weather station and WSN).Validation shows the outperformance of our scheme compared to existing schemes.

Flash Flood Detection in Urban Cities Using Ultrasonic and Infrared Sensors

Floods are the most common type of natural disaster, often leading to loss of lives and properties in the thousands yearly. Among these events, urban flash floods are particularly deadly because of the short timescales on which they occur, and because of the population density of cities. Since most flood casualties are caused by a lack of information on the impending flood (type, location, and severity), sensing these events is critical to generate accurate and detailed warnings and short term forecasts. However, no dedicated flash flood sensing systems, that could monitor the propagation of flash floods, in real time, currently exist in cities. In this paper, first, a new sensing device that can simultaneously monitor urban flash floods and traffic congestion has been presented. This sensing device is based on the combination of ultrasonic range finding with remote temperature sensing, and can sense both phenomena with a high degree of accuracy, using a combination of L1-regularized reconstruction and artificial neural networks to process measurement data. Second, corresponding algorithms have been implemented on a low-power wireless sensor platform, and their performance in water level estimation in a six months test involving four different sensors is illustrated. The results demonstrate that urban water levels can be reliably estimated with error less than 2 cm, and that the preprocessing and machine learning schemes can run in real time on currently available wireless sensor platforms.

A hybrid system approach to airspeed, angle of attack and sideslip estimation in Unmanned Aerial Vehicles

Fixed wing Unmanned Aerial Vehicles (UAVs) are an increasingly common sensing platform, owing to their key advantages: speed, endurance and ability to explore remote areas. While these platforms are highly efficient, they cannot easily be equipped with air data sensors commonly found on their larger scale manned counterparts. Indeed, such sensors are bulky, expensive and severely reduce the payload capability of the UAVs. In consequence, UAV controllers (humans or autopilots) have little information on the actual mode of operation of the wing (normal, stalled, spin) which can cause catastrophic losses of control when flying in turbulent weather conditions. In this article, we propose a real-time air parameter estimation scheme that can run on commercial, low power autopilots in real-time. The computational method is based on a hybrid decomposition of the modes of operation of the UAV. A Bayesian approach is considered for estimation, in which the estimated airspeed, angle of attack and sideslip are described statistically. An implementation on a UAV is presented, and the performance and computational efficiency of this method are validated using hardware in the loop (HIL) simulation and experimental flight data and compared with classical Extended Kalman Filter estimation. Our benchmark tests shows that this method is faster than EKF by up to two orders of magnitude.

Decentralized Energy and Power Estimation in Solar-Powered Wireless Sensor Networks

Solar powered wireless sensor networks are very adapted to smart city applications, since they can operate for extended durations with minimal installation costs. Nonetheless, they require energy management schemes to operate reliably, unlike their grid-powered counterparts. Such schemes require the forecasting of future solar power inputs for each wireless sensor node, over a time horizon. They also require the determination of battery energy parameters in real time. To address both requirements, we propose a collaborative solar power forecasting framework combined to a real time battery capacity estimation model, which can be used to optimize the node schedules over the corresponding horizon.

Vehicle detection and speed estimation with PIR sensors

Reliable and accurate traffic sensing is the basis of Intelligent Transportation Systems (ITS), which mitigate traffic mobility and safety issues. To promote vast adoption of ITS technologies, rapid deployment and auto-calibration of traffic sensing systems are critical. Aiming at the development of an advanced traffic sensing system for construction zones, this poster presents our preliminary results for detecting vehicles and estimating traffic speeds by applying signal processing and machine learning techniques using Passive Infrared (PIR) sensor data.

Wireless Sensor Network-Based Urban Traffic Monitoring Using Inertial Reference Data

Probe vehicle data is currently generated using satellite navigation systems such as the GPS, GLONASS or Galileo systems. However, because of their high cost and relatively high position uncertainty and low sampling rate, satellite positioning systems have a relatively low penetration rate among users. In addition, such sensors do not provide context in the traffic measurements. To address these issues, we introduce a new traffic monitoring concept based on inexpensive inertial measurement units in conjunction with a wireless sensor network deployed inside a city. After discussing the sensing technique, we present a preliminary implementation of this system using an open source robotic platform. Preliminary results show that this system can be used to generate traffic measurement data.

Minimum-Time Attitude Control of Deformable Solar Sails with Model Uncertainty

This paper develops a new algorithm for large-angle pitch maneuvers of deformable solar sails in minimum time that avoids overshooting the target angle given a set of model uncertainties. The paper...

A Privacy-Preserving Urban Traffic Estimation System

This chapter describes a novel traffic monitoring system based on data generated by Inertial Measurement Units (IMUs) in conjunction with short range Bluetooth or WiFi readers. The IMUs are used to estimate the vehicle path along the transportation network, detect traffic stops and go waves, classify traffic-related events, and possibly monitor the condition of the roadway. We introduce a trajectory estimation method for estimating vehicle paths from IMU data and Bluetooth reader position data only. Using this method, we show that the state of traffic on an urban network can be estimated locally by solving a set of independent traffic estimation problems with unknown boundary conditions. This set of independent solutions are then regularized using a consensus-type algorithm to estimate the unknown boundary conditions during the process. This system allows one to estimate the state of traffic over an urban network, while maintaining the privacy of the users, unlike current systems.

Unmanned aerial vehicle path planning for traffic estimation and detection of non-recurrent congestion

Unmanned aerial vehicles (UAVs) provide a novel means of extracting road and traffic information via video data. Specifically, by analyzing objects in a video frame, UAVs can be used to detect traffic characteristics and road incidents. Under congested conditions, the UAVs can supply accurate incident information where it is otherwise difficult to infer the road state from traditional speed-density measurements. Leveraging the mobility and detection capabilities of UAVs, we investigate navigation algorithms that seek to maximize information on the road/traffic state under non-recurrent congestion. We propose an active exploration framework that (1) assimilates UAV observations with speed-density sensor data, (2) quantifies uncertainty on the road/traffic state, and (3) adaptively navigates the UAV to minimize this uncertainty. The navigation algorithm uses the A-optimal information measure (mean uncertainty) and it depends on covariance matrices generated by an ensemble Kalman filter (EnKF). In the EnKF procedure, we incorporate nonlinear traffic observations through model diagnostic variables, and we present a parameter update procedure that maintains a monotonic relationship between states and measurements. We compare the traffic and incident state estimates resulting from the coupled UAV navigation-estimation procedure against corresponding estimates that do not use targeted UAV observations. Our results indicate that UAVs aid in detection of incidents under congested conditions where speed-density data are not informative.