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

A Survivable Communication Game based Approach for a network of cooperative UAVs

Unmanned Aerial Vehicle (UAV) based technologies are suitable for many civil and military application. In case of the occurrence of some natural disasters such as storms, the infrastructure of the communication system plays a vital role in offering rescue services. In such situations, energy and distance limitations can affect the operations of the UAVs and change the efficiency of the UAV network. To overcome intermittent connectivity and unstable links in this highly dynamic topology, a resilient UAV network is needed. In this paper, the communications infrastructure of a multi-UAV network is investigated in term of availability and survivability factors. We utilize the selected path in the multi-level multi-UAV network to avoid problems associated to interference, link failures, energy constrains, and routing. We present a distributed approach, namely: Survivable Communication Game based Approach (SCGA). SCGA works based on a cooperative communication between UAVs to guarantee a reliable data transmission using the best path. First, we build the utility of our approach based on the achievable rate, transmission delay, and energy consumption. Next, we formulate the interactions between UAVs based on a game framework approach. last, we conduct an extensive simulation using different communication scenarios to investigate the performance of the SCGA in terms of survivability, scalability, and reliability. In addition, the communication links and the connectivity between UAVs are tested using different performance metrics. Moreover, we compare SCGA against both of Nearest Neighbor (NN) and Nash Network Formation (NNF) schemes. The simulation results show that SCGA outperforms both of NN and NNF schemes in terms survivability, scalability, and reliability.The authors would like to acknowledge the support of the Computer Engineering Department at King Fahd University of Petroleum and Mineral for this work