Automated Environmental Compliance Monitoring with IoT and Open Government Data

Negative environmental impacts on societies and ecosystems are frequently driven by human activity and amplified by increasing climatic variability. Properly managing these impacts relies on a government's ability to ensure environmental regulatory compliance in the face of increasing uncertainty. Water flow rates are the most widely used evaluation metric for river regulatory compliance. Specifically, compliance thresholds are set by calculating the minimum flow rates required by aquatic species such as fish. These are then designated as the minimum "environmental flows" (eflows) for each river. In this paper, we explore how IoT-generated open government data can be used to enhance the development of an automated IoT-based eflows compliance system. To reduce development and operational costs, the proposed solution relies on routinely collected river monitoring data. Our approach allows for any authority with similar data to rapidly develop, test and verify a scalable solution for eflow regulatory compliance monitoring and evaluation. Furthermore, we demonstrate a real-world application of our system using open government data from Estonia's national river monitoring network. The main novelty of this work is that the proposed IoT-based system provides a simple evaluation tool that re-purposes IoT-generated open government data to evaluate compliance and improve monitoring at a national scale. This work showcases a new paradigm of IoT-based solutions using open government data and provides a real-world example of how the solution can automatically evaluate environmental compliance in increasingly uncertain environments.

[1]  B. R. Shivakoti,et al.  CLIMATE CHANGE ADAPTATION IN WATER SECTOR , 2013 .

[2]  Miguel D. Mahecha,et al.  A typology of compound weather and climate events , 2020, Nature Reviews Earth & Environment.

[3]  P. McIntyre,et al.  Global threats to human water security and river biodiversity , 2010, Nature.

[4]  Diego López-de-Ipiña,et al.  Citizen-centric data services for smarter cities , 2017, Future Gener. Comput. Syst..

[5]  Vishwanath P Baligar,et al.  Low Cost IoT based Flood Monitoring System Using Machine Learning and Neural Networks: Flood Alerting and Rainfall Prediction , 2020, 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA).

[6]  Wei Dong,et al.  Data-Driven Solution for Optimal Pumping Units Scheduling of Smart Water Conservancy , 2020, IEEE Internet of Things Journal.

[7]  Robert Krimmer,et al.  How does open government data driven co-creation occur? Six factors and a 'perfect storm'; insights from Chicago's food inspection forecasting model , 2019, Gov. Inf. Q..

[8]  Niels Bjørn-Andersen,et al.  Data-Driven Innovation through Open Government Data , 2014, J. Theor. Appl. Electron. Commer. Res..

[9]  Li Tian,et al.  Study on Cost-Sensitive Communication Models on Large-scale Monitor Networks , 2010, 2010 International Conference on E-Business and E-Government.

[10]  Sunil Choenni,et al.  Socio-technical Impediments of Open Data , 2012 .

[11]  An Yan,et al.  Civic Hackers' User Experiences and Expectations of Seattle's Open Municipal Data Program , 2017, HICSS.

[12]  Sneha A. Dalvi,et al.  Internet of Things for Smart Cities , 2017 .

[13]  P. Parasiewicz,et al.  “E = mc2” of Environmental Flows: A Conceptual Framework for Establishing a Fish-Biological Foundation for a Regionally Applicable Environmental Low-Flow Formula , 2018, Water.

[14]  Ines Mergel,et al.  Citizen-oriented digital transformation in the public sector , 2018, DG.O.

[15]  Diego López-de-Ipiña,et al.  Collaboration-Centred Cities through Urban Apps Based on Open and User-Generated Data , 2015, UCAmI.

[16]  Roshanak Nateghi,et al.  Multi-Dimensional Infrastructure Resilience Modeling: An Application to Hurricane-Prone Electric Power Distribution Systems , 2018, IEEE Access.

[17]  C. Vaughn Biodiversity Losses and Ecosystem Function in Freshwaters: Emerging Conclusions and Research Directions , 2010 .

[18]  Bengt Ahlgren,et al.  Internet of Things for Smart Cities: Interoperability and Open Data , 2016, IEEE Internet Computing.

[19]  R. Krimmer,et al.  Turning Open Government Data into Public Value: Testing the COPS Framework for the Co-creation of OGD-Driven Public Services , 2019, Governance Models for Creating Public Value in Open Data Initiatives.

[20]  Sharmila,et al.  Automation in Agriculture and IoT , 2019, 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU).

[21]  Tianbao Qin Strategic planning of e-Government System for Disaster Prevention and Relief: A case study , 2011, 2011 International Conference on Computer Science and Service System (CSSS).

[22]  S. A. Timashev Infrastructure Resilience: Definition, calculation, application , 2015, 2015 International Conference on Interactive Collaborative Learning (ICL).

[23]  Gene E. Likens,et al.  Who needs environmental monitoring , 2007 .

[24]  Yannis Charalabidis,et al.  Benefits, Adoption Barriers and Myths of Open Data and Open Government , 2012, Inf. Syst. Manag..