A framework for multimodal wireless sensor networks

Wireless Sensor Networks are a widely used solution for monitoring oriented applications (e.g., water quality on watersheds, pollution monitoring in cities). These kinds of applications are characterized by the necessity of two data-reporting modes: time-driven and event-driven. The former is used mainly for continually supervising an area and the latter for event detection and tracking. By switching between both modes, a WSN can improve its energy-efficiency and event reporting latency, compared to single data-reporting schemes. We refer to those WSNs, where both data-reporting modes are required simultaneously, as MultiModal Wireless Sensor Networks (M2WSNs). M2WSNs arise as a solution for the trade-off between energy savings and event reporting latency in those monitoring-oriented applications where regular and emergency reporting are required simultaneously. The multimodality in these M2WSNs allows sensor nodes to perform data-reporting in two possible schemes, time-driven and event-driven, according to the circumstances, providing higher energy savings and better reporting results when compared to traditional schemes. Traditionally, sophisticated power-aware wake-up schemes have been employed to achieve energy efficiency in WSNs, such as low-duty cycling protocols using a single radio architecture. These protocols achieve good results regarding energy savings, but they suffer from idle-listening and overhearing issues, that make them not reliable for most ultra-low-power demanding applications, especially, those deployed in hostile and unattended environments. Currently, Wake-up Radio Receivers based protocols, under a dual-radio architecture and always-on operation, are emerging as a solution to overcome these issues, promising higher energy consumption reduction and reliability in terms of latency and packet-delivery-ratio compared to classic wake-up protocols. By combining different transceivers and reporting protocols regarding energy efficiency and reliability, multimodality in M2WSNs is achieved. This dissertation proposes a conceptual framework for M2WSNs that integrates the goodness of both data-reporting schemes and the Wake-up Radio paradigm--data periodicity, responsiveness, and energy-efficiency--, that might be suitable for monitoring oriented applications with low bandwidth requirements, that operates under normal circumstances and emergencies. The framework follows a layered approach, where each layer aims to fulfill specific tasks based on its information, the functions provided by its adjacent layers, and the information resulted from the cross-layer interactions.Doctor en IngenieríaDoctoradohttps://orcid.org/0000-0003-1346-6451https://scholar.google.com.co/citations?user=0I4kXQUAAAAJ&hl=enhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=000001365

Comparing Building and Neighborhood-Scale Variability of CO₂ and O₃ to Inform Deployment Considerations for Low-Cost Sensor System Use.

The increased use of low-cost air quality sensor systems, particularly by communities, calls for the further development of best-practices to ensure these systems collect usable data. One area identified as requiring more attention is that of deployment logistics, that is, how to select deployment sites and how to strategically place sensors at these sites. Given that sensors are often placed at homes and businesses, ideal placement is not always possible. Considerations such as convenience, access, aesthetics, and safety are also important. To explore this issue, we placed multiple sensor systems at an existing field site allowing us to examine both neighborhood-level and building-level variability during a concurrent period for CO₂ (a primary pollutant) and O₃ (a secondary pollutant). In line with previous studies, we found that local and transported emissions as well as thermal differences in sensor systems drive variability, particularly for high-time resolution data. While this level of variability is unlikely to affect data on larger averaging scales, this variability could impact analysis if the user is interested in high-time resolution or examining local sources. However, with thoughtful placement and thorough documentation, high-time resolution data at the neighborhood level has the potential to provide us with entirely new information on local air quality trends and emissions

Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary).

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena

Autonomous monitoring framework for resource-constrained environments

Acknowledgments The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub, reference: EP/G066051/1. URL: http://www.dotrural.ac.uk/RemoteStream/Peer reviewedPublisher PD