Implementation of an IoT Based Radar Sensor Network for Wastewater Management

Critical wastewater events such as sewer main blockages or overflows are often not detected until after the fact. These events can be costly, from both an environmental impact and monetary standpoint. A standalone, portable radar device allowing non-invasive benchmarking of sewer pumping station (SPS) pumps is presented. Further, by configuring and deploying a complete Low Power Wide Area Network (LPWAN), Shoalhaven Water (SW) now has the opportunity to create Internet of Things (IoT)-capable devices that offer freedom from the reliance on mobile network providers, whilst avoiding congestion on the existing Supervisory Control and Data Acquisition (SCADA) telemetry backbone. This network infrastructure allows for devices capable of real-time monitoring to alert of any system failures, providing an effective tool to proactively capture the current state of the sewer network between the much larger SPSs. This paper presents novel solutions to improve the current wastewater network management procedures employed by SW. This paper also offers a complete review of wastewater monitoring networks and is one of the first to offer robust testing of Long Range Wide Area Network (LoRaWAN) network capabilities in Australia. The paper also provides a comprehensive summary of the LoRa protocol and all its functions. It was found that a LPWAN, utilising the LoRaWAN protocol and deployed appropriately within a geographic area, can attain maximum transmission distances of 20 km within an urban environment and up to 35 km line of sight

IoT Applications Computing

The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing

IoT-based smart water network management challenges and future trend

Abstract: In recent years, internet of things (IoT)-based tech- nology has been deployed to different areas of water supply system. This is in line with the Smart Water Network Manage- ment (SWNM) initiative, that aims to propose a methodology to improve operational performance and monitoring of water supply systems. In the smart water network management applications, IoT-based technology is facing quite a few challenges. In this paper, we present IoT’s applications for monitoring the water supply network with emphasis on its application to water quality and leakage monitoring. In these applications, the technical challenges of IoT-based technology are also discussed. Finally, future research directions in this domain are also highlighted, which may be useful for further studies

A smart sewer asset information model to enable an ‘Internet of Things’ for operational wastewater management

Real-time prediction of flooding is vital for the successful future operational management of the UK sewerage network. Recent advances in smart infrastructure and the emergence of the Internet of Things (IoT), presents an opportunity within the wastewater sector to harness and report in real-time sewer condition data for operation management. This study presents the design and development of a prototype Smart Sewer Asset Information Model (SSAIM) for an existing sewerage network. The SSAIM, developed using Industry Foundation Class version 4 (IFC4) an open neutral data format for BIM, incorporates distributed smart sensors to enable real-time monitoring and reporting of sewer asset performance. Results describe an approach for sensor data analysis to facilitate the real-time prediction of flooding

Current Advances in Internet of Underground Things

The latest developments in Internet of Underground Things are covered in this chapter. First, the IOUT Architecture is discussed followed by the explanation of the challenges being faced in this paradigm. Moreover, a comprehensive coverage of the different IOUT components is presented that includes communications, sensing, and system integration with the cloud. An in-depth coverage of the applications of the IOUT in various disciplines is also surveyed. These applications include areas such as decision agriculture, pipeline monitoring, border control, and oil wells

Decision Agriculture

In this chapter, the latest developments in the field of decision agriculture are discussed. The practice of management zones in digital agriculture is described for efficient and smart faming. Accordingly, the methodology for delineating management zones is presented. Modeling of decision support systems is explained along with discussion of the issues and challenges in this area. Moreover, the precision agriculture technology is also considered. Moreover, the chapter surveys the state of the decision agriculture technologies in the countries such as Bulgaria, Denmark, France, Israel, Malaysia, Pakistan, United Kingdom, Ukraine, and Sweden. Finally, different field factors such as GPS accuracy and crop growth are also analyzed

Overløpskontroll i avløpsnett med forskjellige modelleringsteknikker og internet of things

Increased urbanization and extreme rainfall events are causing more frequent instances of sewer overflow, leading to the pollution of water resources and negative environmental, health, and fiscal impacts. At the same time, the treatment capacity of wastewater treatment plants is seriously affected. The main aim of this Ph.D. thesis is to use the Internet of Things and various modeling techniques to investigate the use of real-time control on existing sewer systems to mitigate overflow. The role of the Internet of Things is to provide continuous monitoring and real-time control of sewer systems. Data collected by the Internet of Things are also useful for model development and calibration. Models are useful for various purposes in real-time control, and they can be distinguished as those suitable for simulation and those suitable for prediction. Models that are suitable for a simulation, which describes the important phenomena of a system in a deterministic way, are useful for developing and analyzing different control strategies. Meanwhile, models suitable for prediction are usually employed to predict future system states. They use measurement information about the system and must have a high computational speed. To demonstrate how real-time control can be used to manage sewer systems, a case study was conducted for this thesis in Drammen, Norway. In this study, a hydraulic model was used as a model suitable for simulation to test the feasibility of different control strategies. Considering the recent advances in artificial intelligence and the large amount of data collected through the Internet of Things, the study also explored the possibility of using artificial intelligence as a model suitable for prediction. A summary of the results of this work is presented through five papers. Paper I demonstrates that one mainstream artificial intelligence technique, long short-term memory, can precisely predict the time series data from the Internet of Things. Indeed, the Internet of Things and long short-term memory can be powerful tools for sewer system managers or engineers, who can take advantage of real-time data and predictions to improve decision-making. In Paper II, a hydraulic model and artificial intelligence are used to investigate an optimal in-line storage control strategy that uses the temporal storage volumes in pipes to reduce overflow. Simulation results indicate that during heavy rainfall events, the response behavior of the sewer system differs with respect to location. Overflows at a wastewater treatment plant under different control scenarios were simulated and compared. The results from the hydraulic model show that overflows were reduced dramatically through the intentional control of pipes with in-line storage capacity. To determine available in-line storage capacity, recurrent neural networks were employed to predict the upcoming flow coming into the pipes that were to be controlled. Paper III and Paper IV describe a novel inter-catchment wastewater transfer solution. The inter-catchment wastewater transfer method aims at redistributing spatially mismatched sewer flows by transferring wastewater from a wastewater treatment plant to its neighboring catchment. In Paper III, the hydraulic behaviors of the sewer system under different control scenarios are assessed using the hydraulic model. Based on the simulations, inter-catchment wastewater transfer could efficiently reduce total overflow from a sewer system and wastewater treatment plant. Artificial intelligence was used to predict inflow to the wastewater treatment plant to improve inter-catchment wastewater transfer functioning. The results from Paper IV indicate that inter-catchment wastewater transfer might result in an extra burden for a pump station. To enhance the operation of the pump station, long short-term memory was employed to provide multi-step-ahead water level predictions. Paper V proposes a DeepCSO model based on large and high-resolution sensors and multi-task learning techniques. Experiments demonstrated that the multi-task approach is generally better than single-task approaches. Furthermore, the gated recurrent unit and long short-term memory-based multi-task learning models are especially suitable for capturing the temporal and spatial evolution of combined sewer overflow events and are superior to other methods. The DeepCSO model could help guide the real-time operation of sewer systems at a citywide level.publishedVersio

A Smart Waste Management System Framework Using IoT and LoRa for Green City Project

Waste management is a pressing concern for society, requiring substantial labor resources and impacting various social aspects. Green cities strive for achieving a net zero-carbon footprint, including efficient waste management. The waste management system deals with three problems that are interrelated: a) the timely checking of the status of bins to prevent overflow; b) checking the precise location of bins; and c) finding the optimal route to the filled bins. The existing systems fail to satisfy all three problem areas with a single solution. To track the overflow of the bin, the proposed model uses ultrasonic sensors, which are complemented with LoRa to transmit the exact location of the bins in a real-time environment. The existing models are not that efficient at calculating the exact bin-filled status along with the precise location of the bins. The Floyd-Warshall algorithm in the proposed model optimizes waste collection using the Floyd-Warshall algorithm to determine the shortest path. Leveraging low-cost IoT technologies, specifically LoRa modules for data transfer, our solution offers benefits such as simplicity, affordability, and ease of replacement. By employing the Floyd-Warshall algorithm with a time complexity of O (n^3), our method efficiently determines the most optimal waste pickup route, saving time and resources. This study presents a smart waste management solution utilising Arduino UNO microcontrollers, ultrasonic sensors, and LoRaWAN to measure waste levels accurately. The proposed strategy aims to create clean and pollution-free cities by addressing the problem of waste distribution caused by poor collection techniques

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users