Universal Solar Powered Water Quality Monitoring IoT Device and Notification System

Water constituents are often event-driven so concentrations and properties vary strongly in time. Due to this, there is a high demand for devices which can get accurate and real time measurements as these changes occur. Current methods of monitoring water quality mostly involve a team of people who collect samples which are later analyzed in a laboratory. This process is time consuming, expensive and has a slow reaction time which may lead to missing out on important changes in the water parameters. This paper describes a prototype solution which is affordable and capable of producing quick and accurate results in real time. This prototype measures the water quality using various sensors which record the pH level, temperature, total dissolved solids, conductivity and changes in the level of water. These sensors are connected to the ESP32 DevKit V4 microcontroller which processes and transmits the data in real time using Wi-Fi to the thinger.io online monitoring dashboard. This dashboard also stores all the data so it can be for analyzing the trends in changes of water quality. In addition to that, this prototype utilizes solar energy harvesting allowing it to be self-sufficiently powered throughout the year.Water constituents are often event-driven so concentrations and properties vary strongly in time. Due to this, there is a high demand for devices which can get accurate and real time measurements as these changes occur. Current methods of monitoring water quality mostly involve a team of people who collect samples which are later analyzed in a laboratory. This process is time consuming, expensive and has a slow reaction time which may lead to missing out on important changes in the water parameters. This paper describes a prototype solution which is affordable and capable of producing quick and accurate results in real time. This prototype measures the water quality using various sensors which record the pH level, temperature, total dissolved solids, conductivity and changes in the level of water. These sensors are connected to the ESP32 DevKit V4 microcontroller which processes and transmits the data in real time using Wi-Fi to the thinger.io online monitoring dashboard. This dashboard also stores all the data so it can be for analyzing the trends in changes of water quality. In addition to that, this prototype utilizes solar energy harvesting allowing it to be self-sufficiently powered throughout the year

Software defined wireless sensor networks application opportunities for efficient network management : a survey

Wireless Sensor Networks (WSNs) are commonly used information technologies of modern networking and computing platforms. Today's network computing applications are faced with a high demand of powerful network functionalities. Functional network reach is central to customer satisfaction such as in mobile networks and cloud computing environments. However, efficient management of WSNs remains a challenge, due to problems supplemental to them. Recent technology shift proposes Software Defined Networking (SDN) for improving computing networks. This review paper highlights application challenges faced by WSNs for monitored environments and those faced by the proposed approaches, as well as opportunities that can be realized on applications of WSNs using SDN. We also highlight Implementation considerations by focusing on critical aspects that should not be disregarded when attempting to improve network functionalities. We then propose a strategy for Software Defined Wireless Sensor Network (SDWSN) as an effort for application improvement in monitored environments.The National Research Foundation (NRF) of South Africa (grant number: RDYR160404161474 and IFR160118156967).http://www.elsevier.com/locate/compeleceng2019-02-01hj2018Electrical, Electronic and Computer Engineerin

Flexible network management in software defined wireless sensor networks for monitoring application systems

Wireless Sensor Networks (WSNs) are the commonly applied information technologies of modern networking and computing platforms for application-specific systems. Today’s network computing applications are faced with high demand of reliable and powerful network functionalities. Hence, efficient network performance is central to the entire ecosystem, more especially where human life is a concern. However, effective management of WSNs remains a challenge due to problems supplemental to them. As a result, WSNs application systems such as in monitored environments, surveillance, aeronautics, medicine, processing and control, tend to suffer in terms of capacity to support compute intensive services due to limitations experienced on them. A recent technology shift proposes Software Defined Networking (SDN) for improving computing networks as well as enhancing network resource management, especially for life guarding systems. As an optimization strategy, a software-oriented approach for WSNs, known as Software Defined Wireless Sensor Network (SDWSN) is implemented to evolve, enhance and provide computing capacity to these resource constrained technologies. Software developmental strategies are applied with the focus to ensure efficient network management, introduce network flexibility and advance network innovation towards the maximum operation potential for WSNs application systems. The need to develop WSNs application systems which are powerful and scalable has grown tremendously due to their simplicity in implementation and application. Their nature of design serves as a potential direction for the much anticipated and resource abundant IoT networks. Information systems such as data analytics, shared computing resources, control systems, big data support, visualizations, system audits, artificial intelligence (AI), etc. are a necessity to everyday life of consumers. Such systems can greatly benefit from the SDN programmability strategy, in terms of improving how data is mined, analysed and committed to other parts of the system for greater functionality. This work proposes and implements SDN strategies for enhancing WSNs application systems especially for life critical systems. It also highlights implementation considerations for designing powerful WSNs application systems by focusing on system critical aspects that should not be disregarded when planning to improve core network functionalities. Due to their inherent challenges, WSN application systems lack robustness, reliability and scalability to support high computing demands. Anticipated systems must have greater capabilities to ubiquitously support many applications with flexible resources that can be easily accessed. To achieve this, such systems must incorporate powerful strategies for efficient data aggregation, query computations, communication and information presentation. The notion of applying machine learning methods to WSN systems is fairly new, though carries the potential to enhance WSN application technologies. This technological direction seeks to bring intelligent functionalities to WSN systems given the characteristics of wireless sensor nodes in terms of cooperative data transmission. With these technological aspects, a technical study is therefore conducted with a focus on WSN application systems as to how SDN strategies coupled with machine learning methods, can contribute with viable solutions on monitoring application systems to support and provide various applications and services with greater performance. To realize this, this work further proposes and implements machine learning (ML) methods coupled with SDN strategies to; enhance sensor data aggregation, introduce network flexibility, improve resource management, query processing and sensor information presentation. Hence, this work directly contributes to SDWSN strategies for monitoring application systems.Thesis (PhD)--University of Pretoria, 2018.National Research Foundation (NRF)Telkom Centre of ExcellenceElectrical, Electronic and Computer EngineeringPhDUnrestricte