Distributed chemical sensor networks for environmental sensing

Society is increasingly accustomed to instant access to real-time information, due to the ubiquitous use of the internet and web-based access tools. Intelligent search engines enable huge data repositories to be searched, and highly relevant information returned in real time. These repositories increasingly include environmental information related to the environment, such as distributed air and water quality. However, while this information at present is typically historical, for example, through agency reports, there is increasing demand for real-time environmental data. In this paper, the issues involved in obtaining data from autonomous chemical sensors are discussed, and examples of current deployments presented. Strategies for achieving large-scale deployments are discussed

Sensing as a Service Model for Smart Cities Supported by Internet of Things

The world population is growing at a rapid pace. Towns and cities are accommodating half of the world's population thereby creating tremendous pressure on every aspect of urban living. Cities are known to have large concentration of resources and facilities. Such environments attract people from rural areas. However, unprecedented attraction has now become an overwhelming issue for city governance and politics. The enormous pressure towards efficient city management has triggered various Smart City initiatives by both government and private sector businesses to invest in ICT to find sustainable solutions to the growing issues. The Internet of Things (IoT) has also gained significant attention over the past decade. IoT envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities. Today infrastructure, platforms, and software applications are offered as services using cloud technologies. In this paper, we explore the concept of sensing as a service and how it fits with the Internet of Things. Our objective is to investigate the concept of sensing as a service model in technological, economical, and social perspectives and identify the major open challenges and issues.Comment: Transactions on Emerging Telecommunications Technologies 2014 (Accepted for Publication

Web-based monitoring of gas emissions from landfill sites using autonomous sensing platforms

Executive Summary Numerous initiatives that are policy driven by national, European and global agencies target the preservation of our environment, human society’s health and our ecology. Ireland’s EPA 2020 Vision outlines a mandate to prepare for the unavoidable impact of climate change, the reduction of greenhouse gas (GHG) emissions, the control of air-emissions standards, the sustainable use of resources and the holding to account of those who flout environmental laws. These strategies are echoed in the Europe 2020: Resource-efficient Europe Flagship Initiative, which also advocates the creation of new opportunities for economic growth and greater innovation. The promotion of research and technical development is central to each of these strategies – specifically the achievement of accurate environmental monitoring technologies that will inform policy-makers and effect change. This is described in the EPA Strategic Plan 2013–2015 as the provision of ‘high quality, targeted and timely environmental data, information and assessment to inform decision making at all levels’. Specific to landfills, the Environmental Protection Agency’s (EPA) Focus on Landfilling in Ireland stipulates the management of landfill gas to eliminate environmental harm and public nuisance, to promote energy generation where possible and to avoid liabilities in site closure and aftercare. It was in this context that the EPA STRIVE programme granted funding for this research project on developing autonomous sensor platforms for the real-time monitoring of gases generated in landfill facilities. Managing landfill gas is one of the crucial operations in a landfill facility, where gases (primarily methane [CH4] and carbon dioxide [CO2] generated from the decomposition of biodegradable waste) are extracted and combusted in a flare or preferably an engine (as biogas fuel). These gases, classified as greenhouse gases (GHGs), also pose localised hazards due to fire risk and asphyxiation, and are indicative of odorous nuisance compounds. Gas-monitoring on site is conducted to (i) ensure against gas migration into the local environment and to (ii) maintain the thorough gas extraction and optimum composition for combustion. This is becoming more relevant because of the numerous landfill closures brought by Europe-wide changes in waste-management policy. Even for landfills no longer actively receiving waste, substantial gas generation remains ongoing for years and even decades. Despite diminished financial resources and reduced manpower, management of this gas must be maintained. Traditionally, monitoring involves taking manual measurements using expensive handheld equipment and requiring laborious travel over difficult and expansive terrain. Consequently, it is conducted relatively infrequently – typically once a month. These issues can be addressed by adopting distributed continuous monitoring systems. These low-cost remotely deployable sensor platforms offer a valuable complementary service to operators and the EPA. They enable easier adherence to their licence criteria, the prevention of expensive remediation measures and the potential boost in revenue from increasing energy production through the use of biogas. Challenges arise in terms of achieving a long-term monitoring performance in a harsh environment while maintaining accuracy, reliability and cost-effectiveness. To meet these challenges, this project developed cost- effective autonomous sensor platforms to allow long- term continuous monitoring of gas composition (methane and carbon dioxide) and extraction pressure. The project’s work represents one of the only developments of autonomous sensor technology in this space; the few other market alternatives tend to be expensive or difficult to implement for remotely deployable continuous monitoring. Beyond the development of a platform technology, the challenge was to apply this technology to the adverse environmental conditions. The project delivered a total of 14 autonomous sensor platforms in deployments involving Irish landfill sites, a Scottish landfill site and a Brazilian wastewater treatment plant. The analysis and interpretation of acquired data, coupled with local meteorological data and on-site operational data, provided the translation from raw environmental data to meaningful conclusions that could inform decision-making. This report presents a number of case studies to illustrate this. Characteristics of site gas dynamics could be identified; for example, it was possible to show if excessive gas concentrations in a perimeter well could be resolved by increasing the flare extraction rate for a particular well. Furthermore, the potential for quantifying methane generation potential at distributed locations within the landfill was identified in addition to diagnosing the effectiveness of the extraction network – hence aiding in field-balancing and landfill gas utilisation. The extensive wealth of data enabled by this platform technology will help better-informed decision-making and improve operational practices in managing gas emissions. In landfills, this signifies alleviating gas migration with perimeter monitoring and enhancing flare/ engine operation by evaluating gas quality at distributed locations within the gas field. While landfilling is becoming outmoded as a waste-management process, the need for continuous monitoring will be relevant for many years to come. Indeed, a number of existing facilities are considering retrofitting engines because of the significant potential for additional landfill gas utilisation being identified by Sustainable Energy Authority Ireland in 2010. Furthermore, the technology’s low-cost and autonomous nature would benefit the hundreds of historical and legacy landfills if any were deemed to be problematic in terms of their environmental impact. Beyond landfills, this work pertains to other applications within the waste sector, as demonstrated by measuring emissions from wastewater treatment plant lagoons. With some further development, this technology could apply to efforts in dealing with climate change (e.g. in evaluating GHG inventories), where applications include managed peatlands (one case study is presented in this report and future efforts could also be targeted at carbon sinks/storage) and agriculture (Ireland’s greatest contributor to GHGs). Further scope could also be pursued in air-quality monitoring, particularly relevant at present with 2013 being dubbed the ‘Year of Air’ by European leaders. Throughout this project, the commercial prospect of this technology was affirmed with positive feedback from landfill operators, environmental regulators and private consultancies. Continual technical developments and refinements in mechanical/electronic design delivered a platform with expanded functionality and reduced price-point, thus becoming more viable for scaled-up deployments and commercial feasibility. Ultimately, this innovative development shows good promise as a high-potential commercial venture, with this work continuing under Enterprise Ireland’s Commercialisation Fund

The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

Distributed environmental monitoring

With increasingly ubiquitous use of web-based technologies in society today, autonomous sensor networks represent the future in large-scale information acquisition for applications ranging from environmental monitoring to in vivo sensing. This chapter presents a range of on-going projects with an emphasis on environmental sensing; relevant literature pertaining to sensor networks is reviewed, validated sensing applications are described and the contribution of high-resolution temporal data to better decision-making is discussed

Efeitos do solo e clima numa vinha de uva de mesa com cultura de cobertura. Gestão da rega utilizando redes de sensores

[ENG] TThe use of mulches in vineyards and orchards is a traditional agricultural practice used with the aim of saving moisture, reducing weed growth and improving organic matter content in the soil. In table grape vineyards trained to overhead system in Puglia region (Southeastern Italy), plastic sheets covering the canopy are often used to either advance ripening or delay harvest. In this environment, the living mulches could contribute to the modification of the microclimate around the canopy below the plastic sheets. This condition has an influence on the climatic demand and on both the vegetative and productive activities, mainly in stages with a high evapotranspiration. However, the presence of living mulches could increase the demand of available water and nutrient resources and this could cause a lower yield. The aim of this study was to acquire a suitable knowledge to manage irrigation and verify the influences of living mulches on the vine by using wireless sensor networks to measure the vapor pressure deficit, soil water potential and content.[POR] A utilização de coberturas do solo em vinhas e pomares é uma prática agrícola tradicional, utilizada com o objetivo de preservar a humidade do solo, reduzir o crescimento de infestantes e melhorar o teor de matéria orgânica no solo. Em vinhas de uva de mesa, conduzidas em sistema de pérgula na região de Puglia (sudeste da Itália), são frequentemente usadas coberturas de plástico para promover o avanço da maturação ou o atraso da colheita. Neste ambiente a utilização de enrelvamentos pode contribuir para a modificação do microclima do copado. Esta condição pode influenciar a demanda atmosférica, bem como a atividade vegetativa e reprodutiva da videira, principalmente em períodos de elevada evapotranspiração. No entanto, a presença do enrelvamento pode originar um aumento da demanda dos recursos disponíveis, nomeadamente água e nutrientes, o que poderá provocar uma quebra de produção. O objetivo deste estudo foi adquirir conhecimento para a gestão da rega e, simultaneamente, verificar a influência dos enrelvamentos na atividade da videira, usando para o efeito redes de sensores “sem fio” para medir o déficit de pressão de vapor, o potencial e o conteúdo de água no solo.The development of this work was supported by: The Spanish Ministry of Science and Innovation through the project RIDEFRUT (ref. AGL2013-49047-C2-1-R), the “Fundacion Seneca, Agencia de Ciencia y Tecnologia” of the Region of Murcia under the “Excelence Group Program”, and the Technical University of Cartagena under the PMPDI Program

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset

Smart Water Infrastructures Laboratory: Reconfigurable Test-Beds for Research in Water Infrastructures Management

The smart water infrastructures laboratory is a research facility at Aalborg University, Denmark. The laboratory enables experimental research in control and management of water infrastructures in a realistic environment. The laboratory is designed as a modular system that can be configured to adapt the test-bed to the desired network. The water infrastructures recreated in this laboratory are district heating, drinking water supply, and waste water collection systems. This paper focuses on the first two types of infrastructure. In the scaled-down network the researchers can reproduce different scenarios that affect its management and validate new control strategies. This paper presents four study-cases where the laboratory is configured to represent specific water distribution and waste collection networks allowing the researcher to validate new management solutions in a safe environment. Thus, without the risk of affecting the consumers in a real network. The outcome of this research facilitates the sustainable deployment of new technology in real infrastructures

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller