Water Contaminants Detection Using Sensor Placement Approach in Smart Water Networks

Incidents of water pollution or contamination have occurred repeatedly in recent years, causing significant disasters and negative health impacts. Water quality sensors need to be installed in the water distribution system (WDS) to allow real-time water contamination detection to reduce the risk of water contamination. Deploying sensors in WDS is essential to monitor and detect any pollution incident at the appropriate time. However, it is impossible to place sensors on all nodes of the network due to the relatively large structure of WDS and the high cost of water quality sensors. For that, it is necessary to reduce the cost of deployment and guarantee the reliability of the sensing, such as detection time and coverage of the whole water network. In this paper, a dynamic approach of sensor placement that uses an Evolutionary Algorithm (EA) is proposed and implemented. The proposed method generates a multiple set of water contamination scenarios in several locations selected randomly in the WDS. Each contamination scenario spreads in the water networks for several hours, and then the proposed approach simulates the various effect of each contamination scenario on the water networks. On the other hand, the multiple objectives of the sensor placement optimization problem, which aim to find the optimal locations of the deployed sensors, have been formulated. The sensor placement optimization solver, which uses the EA, is operated to find the optimal sensor placements. The effectiveness of the proposed method has been evaluated using two different case studies on the example of water networks: Battle of the Water Sensor Network (BWSN) and another real case study from Madrid (Spain). The results have shown the capability of the proposed method to adapt the location of the sensors based on the numbers and the locations of contaminant sources. Moreover, the results also have demonstrated the ability of the proposed approach for maximising the coverage of deployed sensors and reducing the time to detect all the water contaminants using a few numbers of water quality sensor

Implementation of a wireless sensor network for agricultural monitoring for Internet of Things (IoT)

The agricultural sector is the first industry and the most impacted by the climate changes. The delicate environment that must manage some type of crops require d the constant monitoring and maintenance of the greenhouse. The Internet of Things (IoT) gives a new alternative for real time environmental monitoring of variables such as temperature, humidity and solar irradiation that can contribute for the health y growth of the crop , and also impact for the plagu es and sickness presence . The objective of this thesis is built a Wireless S ensor N etwork using radiofrequency devices and environmental sensor s . The limitation s of this master thesis are: the location of the sensor node , the external conditions that wi ll not impact the network, the simulation, test and pilots that are deployed in a controlled space . The wireless sensor network proposed employ s the Zolertia Motes using IEEE802.15.4 standard . This device allows low power consumption , as the nodes must be located in places where it may handle several week s without change depending on the autonomy of the ir batteries cell . The network protocol manage d works over l ow consumption , as same as the transmitted and received packets of data . T he standard used on this project is the 6LowPAN . T he network co nfigured works over the stack protocol IPv6 so that all the devices handled UDP and manage this internet package. The Raspberry Pi 3 B will work as border router between the sensor nodes and the exterior consider ed as Internet using the IPv4 standard internet router protocol. The framework used for the network implementation is ContikiOS installed on th e gateway and tested using one mote located in the la b . The data manage d in this experiment has low data rate as this measurement do not require a permanent monitoring and high speed . T he atmospherically changes are not variant enough to be observe d constantly . The sample rate will be 1 package each 10 minutes. This project aim s to develop a full network implementa tion since the mote until the dashboard