Considerations on the Deployment of Heterogeneous IoT Devices For Smart Water Networks

Abstract

Water distribution systems are seeing the increased deployment of new technologies that use Internet of Things (IoT) to gather, analyse and extract useful information from data; further enabling Smart Water Networks (SWNs). IoT type technologies have a huge potential to enable more efficient water resources management. Heterogeneous IoT sensors/devices/technologies from different vendors are starting to be employed in SWNs. The deployment of IoT sensors is a critical issue that significantly affects a wireless sensors network's real-time monitoring performance. The aim of the deployment of IoT sensors (i.e. nodes) for wireless networks is to find the most efficient number, type and locations of sensors that enable satisfying a SWN's hydraulic and water quality requirements (among the others) while considering issues such as energy consumption of the nodes and coverage of the target monitoring area, to mention just a few. This paper provides an overview of the SWN architecture and its components and scrutinises emerging sensor and communication technologies for developing SWNs focussing on the sensing and control, and collection and communication layers of the SWN architecture. Additionally, it presents a selective review of recent literature on issues related to the intelligent deployment of sensors in wireless sensors networks and on sensors deployment for water quality and leak detection/localisation applications in SWNs. The main aim of the literature review carried out and presented here has been to highlight the main challenges of developing IoT-enabled SWNs, so that interesting future research directions for achieving a context-aware IoT framework for SWNs can be identified.

Distance-Based Queuing for Scalable and Reliable Linear Wireless Sensor Networks in Smart Cities

“…For example, LWSNs will play a crucial role in Smart City applications, such as Smart Water Management (SWM) systems [8,9], where pipelines are sensed in a high number of points to measure water-quality parameters, the state of the infrastructure, and consumption. However, as a result of the large amount of traffic generated, the current supporting networks tend to be overloaded, limiting the reliability and scalability of the application [10,11].…”

Deep Learning for Water Quality Classification in Water Distribution Networks

“…The two fundamental parts in developing these systems are sensor placement methods in the WDS, and the analysis of the big volume of data generated from these sensors [4] [5]. Typically, sensors generate large amounts of data streams, which need to be analyzed in real-time to detect abnormal events that cause significant water pollution in WDS [6]. A large number of machine learning and statistical models for classification have been proposed, such as regularized discriminant analysis (RDA) [7], linear discriminant analysis (LDA) [8], quadratic discriminant analysis (QDA) [9], support vector machines (SVM) [10], neural networks (NN) [11], and k-nearest neighbour classifier (KNN) [12].…”