Abstract-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 This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions.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.Index Terms-wireless sensor networks, agricultural activities, water quality monitoring and management, catchment, collaborative. INTRODUCTIONWater is a key natural resource which is vital for the survival of all ecosystems on the planet. However, less than 1% of the earth's water resources are accessible to humans as fresh water, in the form of either surface or ground water (Krchnak et al., 2002, UNESCO, 2006. Although there is currently sufficient water for essential activities (Blanco et al., 2009) including drinking, irrigation, and domestic and industrial use on a global scale, the spatial distribution of water suggests that, in many cases, it is not available where it is required. Because of the unequal distribution of fresh water resources, billions of people around the globe live in water-stressed and water-limited environments. Therefore it is crucial to preserve water resources although in practice it is continually degraded and depleted owing to inappropriately targeted funding initiatives leading to poor water management, redundant and outdated agricultural practices and urban development (Rosegrant et al., 2002, Verhoeven et al., 2006. The key issues relating to global freshwater quality problems in the environment and public hea...

Irrigated agriculture provides 40% of the World's food from 20% of the agricultural land but uses 70% of all global freshwater withdrawals. However, even supposedly efficient and well-managed irrigation systems waste up to 50% of the water applied to the crops under them. Meeting the food needs of an increasing world population from a static or even decreasing land base will, therefore require improved efficiencies in irrigated agriculture and better use of these finite water resources. The first part of this paper reports on a field-based research project which examined a suite of conventional and alternative irrigation systems which were installed at a farm in south west Australia and assessed and compared in terms of their Water Use Efficiency. All "alternative" systems outperformed the conventional surface (flood) irrigation systems with comparative water savings of around 50%. The second part of the paper assesses the potential Water Use Efficiency improvements at farm and system-scales which could be achieved through linking these irrigation systems to wireless soil-moisture sensor networks which are being developed by the authors and which are reported in detail in associate papers. Improving irrigation scheduling and management by better (and, where appropriate, automatic) links to near real-time soil moisture data is shown to produce water savings of up to 30 GL per year at the irrigation system scale.

Over recent years, the demand for supplies of freshwater is escalating with the increasing food demand of a fast-growing population. The agriculture sector of Pakistan contributes to 26% of its GDP and employs 43% of the entire labor force. However, the currently used traditional farming methods such as flood irrigation and rotating water allocation system (Warabandi) results in excess and untimely water usage, as well as low crop yield. Internet of things (IoT) solutions based on real-time farm sensor data and intelligent decision support systems have led to many smart farming solutions, thus improving water utilization. The objective of this study was to compare and optimize water usage in a 2-acre lemon farm test site in Gadap, Karachi, for a 9-month duration, by deploying an indigenously developed IoT device and an agriculture-based decision support system (DSS). The sensor data are wirelessly collected over the cloud and a mobile application, as well as a web-based information visualization, and a DSS system makes irrigation recommendations. The DSS system is based on weather data (temperature and humidity), real time in situ sensor data from the IoT device deployed in the farm, and crop data (Kc and crop type). These data are supplied to the Penman–Monteith and crop coefficient model to make recommendations for irrigation schedules in the test site. The results show impressive water savings (~50%) combined with increased yield (35%) when compared with water usage and crop yields in a neighboring 2-acre lemon farm where traditional irrigation scheduling was employed and where harsh conditions sometimes resulted in temperatures in excess of 50 °C.

This paper reports on the validation of a simplified discharge prediction model that is suitable for implementation on a resourced constrained system such as a wireless sensor network, which will allow their operation to become more proactive rather than reactive. The data-driven model, utilising an M5 decision tree modelling technique, is validated using a 12-month training data set derived from published measured data. Daily runoff and drainage is predicted, and the results are compared with existing data-driven models developed in this domain. Results for the model give an R 2 of 0.82 and Root Relative Mean Square Error (RRMSE) of 35.9%. 80% of the residuals for the predicted test values fall within a +2 mm discharge depth/day error range. The main significance is that the proposed model gives comparable results with fewer samples and simpler parameters when compared to previous published research, which offers the potential for implementation in resource constrained monitoring and control systems.

Unmanned aerial vehicles (UAVs) have emerged as a rapidly growing technology seeing unprecedented adoption in various application sectors due to their viability and low cost. However, UAVs have also been used to perform illegal and malicious actions, which have recently increased. This creates a need for technologies capable of detecting, classifying, and deactivating malicious and unauthorized drones. This paper reviews the trends and challenges of the most recent UAV detection methods, i.e., radio frequency-based (RF), radar, acoustic, and electro-optical, and localization methods. Our research covers different kinds of drones with a major focus on multirotors. The paper also highlights the features and limitations of the UAV detection systems and briefly surveys the UAV remote controller detection methods.