Gonçalo Jesus

A Survey on Data Quality for Dependable Monitoring in Wireless Sensor Networks

Wireless sensor networks are being increasingly used in several application areas, particularly to collect data and monitor physical processes. Non-functional requirements, like reliability, security or availability, are often important and must be accounted for in the application development. For that purpose, there is a large body of knowledge on dependability techniques for distributed systems, which provide a good basis to understand how to satisfy these non-functional requirements of WSN-based monitoring applications. Given the data-centric nature of monitoring applications, it is of particular importance to ensure that data are reliable or, more generically, that they have the necessary quality. In this survey, we look into the problem of ensuring the desired quality of data for dependable monitoring using WSNs. We take a dependability-oriented perspective, reviewing the possible impairments to dependability and the prominent existing solutions to solve or mitigate these impairments. Despite the variety of components that may form a WSN-based monitoring system, we give particular attention to understanding which faults can affect sensors, how they can affect the quality of the information and how this quality can be improved and quantified.

Operational forecast framework applied to extreme sea levels at regional and local scales

ABSTRACT The design, implementation and demonstration of a novel and generic computational forecast framework for multi-scale prediction of extreme sea levels and associated flooding is presented. Denoted Water Information Forecast Framework (WIFF), it integrates process-based models for waves, tides and surges from regional to local scales, predicting the flooding of coastal areas, and supporting the routine and emergency management of coastal resources. WIFF manages the simulations and the real-time monitoring data, archives the data and makes the information available through a WebGIS that targets users with distinct access privileges. Additionally, the web component of WIFF adapts automatically and transparently to any device. WIFF also provides ways to assess the model accuracy and generates tailored products based on model results and observations. WIFF is demonstrated in the prediction of extreme water levels in the Portuguese coast, simulating processes at different scales: at basin scales, waves are simulated in the North Atlantic and in the Portuguese shelf, and sea levels due to tides and atmospheric forcings are simulated in the Northeast Atlantic; at estuarine scales, high-resolution, fully coupled wave/circulation predictions are performed in the Tagus estuary to account for wave–current interactions. User-oriented georeferenced products are generated, including automatic model/data comparisons, targeting the needs of civil protection agents and combining for the first time an agile, service-oriented platform with high-resolution, process-rich predictions of the Tagus dynamics.

Towards Dependable Measurements in Coastal Sensors Networks

A flood monitoring system incorporates water sensor networks, forecast simulations models, and a decision-support web-based system. The objective of the system is to achieve reliable flood protection and response. This is challenging because of the inherent presence of a cascade of uncertainties in the forecast models, and also uncertainties affecting the timeliness and quality of raw sensor data that is used in the forecasting processes. Achieving real-time and accurate data collection is difficult due to the pervasive nature of the monitoring networks and because sensors and sensor nodes are vulnerable to external disturbances affecting data accuracy. In this paper we motivate for the need of dependable data collection in harsh coastal and marine environments, we overview the main challenges that need to be addressed and we introduce some initial ideas on what needs to be done in order to deal with external disturbances causing faulty measurements.

Managing a Coastal Sensors Network in a Nowcast-Forecast Information System

This paper presents a study on the integration of heterogeneous sensor nodes into a wireless sensor network and its use in providing real-time information about water conditions, contributing to water resources management. The work focuses on presenting the methodology used to automatically communicate, parse and store the sensor network data and its application to a real case study. The development of a new platform for efficient management and analysis of the gathered data and its use for automatic assertion of real-time model forecasts quality against stored data is also presented. On-going work on the development of a mobile application for real-time data access in mobile devices is also anticipated.

An Innovative Web Platform for Flood Risk Management

This paper presents an innovative real-time information system for enhanced support to flood risk emergency in urban and nearby coastal areas, targeting multiple users with distinct access privileges. The platform addresses several user requirements such as (1) fast, online access to relevant georeferenced information from wireless sensors, high-resolution forecasts and comprehensive risk analysis; and, (2) the ability for a two-way interaction with civil protection agents in the field. The platform adapts automatically and transparently to any device with data connection. Given its specific purpose, both data protection and tailored-to-purpose products are accounted for through user specific access roles. This paper presents the platform’s overall architecture and the technologies adopted for server-side, focusing on communication with the front-end and with the wireless sensor network, and the user interface development, using state-of-the-art frameworks for cross-platform standardized development. The advantages of the adopted solution are demonstrated for the Tagus estuary inundation information system.

Dependable Outlier Detection in Harsh Environments Monitoring Systems

Environmental monitoring systems are composed by sensor networks deployed in uncertain and harsh conditions, vulnerable to external disturbances, posing challenges to the comprehensive system characterization and modelling. When unexpected sensor measurements are produced, there is a need to detect and identify, in a timely manner, if they stem from a failure behavior or if they indeed represent some environment-related process. Existing solutions for fault detection in environmental sensor networks do not portray the required sensitivity for the differentiation of these processes or they are unable to meet the time constraints of the affected cyber-physical systems.

Implementation of an early warning system in urban drainage infrastructures for direct discharges and flood risk management

Combined sewer systems are often unable to respond adequately to rising water volumes draining from urban areas during rainfall events, resulting in frequent direct discharges into receiving waters and floods, with severe environmental and economic impacts. Despite stricter legislation on pollution control and flood risk assessment, there are still some challenges regarding the development of early warning systems based on water quality issues and fully integrated models. An innovative, real-time urban warning system for flooding and pollution events was built for the Alcântara basin (the largest in Lisbon), to provide timely information to wastewater management entities and to civil protection services. The platform provides real-time access to monitoring data and, based on 48-hour precipitation forecasts, predicts the performance of the system through the integrated use of mathematical models for both drainage network and estuary. Predictions are automatically compared and validated with on-line data. This paper presents the overall design of the system and main results obtained thus far. The analysis of the system shows the ability of the integrated models to represent the main spatial and temporal patterns observed, effectively predicting the system response to precipitation events and estimating volumes discharged into the water bodies and their average pollution loads. Furthermore, the overall