Minimum transport-driven algorithm for water distribution network partitioning

This paper presents a novel algorithm driven by the minimization of the transport function for the partitioning of water distribution networks (WDNs) into district metered areas (DMAs). The algorithm is based on the linear programming (LP) embedded inside a multi-objective genetic algorithm, which enables engineering criteria, such as the minimization of the boundary pipes and the maximization of the uniformity of DMAs, to be considered in the partitioning. Furthermore, the application of the algorithm on the dual network topology based on segments and valves guarantees that configurations of DMAs that respect the real positions of isolation valves for WDN partitioning are obtained. After being described on a small WDN, it is successfully validated on a large size WDN, proving better performance than other algorithms in the scientific literature for the generation of engineeringly appealing DMA configurations, with almost identical hydraulic performance to the unpartitioned WDN.

Contact ability based topology control for predictable delay-tolerant networks

In predictable delay tolerant networks (PDTNs), the network topology is known a priori or can be predicted over time, such as space planet networks and vehicular networks based on public buses or trains. Due to the intermittent connectivity, network partitioning, and long delays in PDTNs, most of the researchers mainly focuses on routing and data access research. However, topology control can improve energy effectiveness and increase the communication capacity, thus how to maintain the dynamic topology of PDTNs becomes crucial. In this paper, a contact ability based topology control method for PDTNs is proposed. First, the contact ability is calculated using our contact ability calculation model, and then the PDTNs is modeled as an undirected weighted contact graph which includes spatial and contact ability information. The topology control problem is defined as constructing a minimum spanning tree (MST) that the contact ability of the MST is maximized. We propose two algorithms based on undirected weighted contact graph to solve the defined problem, and compare them with the latest method in terms of energy cost and contact ability. Extensive simulation experiments demonstrate that the proposed algorithms can guarantee data transmission effectively, and reduce the network energy consumption significantly.

Graph Neural Network for Integrated Water Network Partitioning and Dynamic District Metered Areas

Water distribution systems (WDSs) are used to transmit and distribute water resources in cities. Water distribution networks (WDNs) are partitioned into district metered areas (DMAs) by water network partitioning (WNP), which can be used for leak control, pollution monitoring, and pressure optimization in WDS management. In order to overcome the limitations of optimal search range and the decrease of recovery ability caused by two-step WNP and fixed DMAs in previous studies, this study developed a new method combining a graph neural network to realize integrated WNP and dynamic DMAs to optimize WDS management and respond to emergencies. The proposed method was tested in a practical case study; the results showed that good hydraulic performance of the WDN was maintained and that dynamic DMAs demonstrated excellent stability in emergency situations, which proves the effectiveness of the method in WNP.

Spatial Planning Insights for Philippine Coral Reef Conservation Using Larval Connectivity Networks

The marine habitats of the Philippines are recognized to be some of the most biodiverse systems globally yet only 1.7% of its seas are designated as marine protected areas (MPAs) with varying levels of implementation. Many of these MPAs were established based on local-scale conservation and fisheries objectives without considering larger-scale ecological connections. The connectivity of reefs through larval dispersal is important in the regional-scale resilience against anthropogenic disturbances and is considered a significant criterion in planning for MPAs. In this study, we provide insights into the delineation of ecologically connected MPA networks using larval dispersal modeling and network analysis. We characterized the network properties of the Philippine coral reefs, organized as 252 reef nodes, based on the larval connectivity networks of a branching coral, sea urchin, and grouper. We then evaluated the distribution of the existing 1,060 MPAs relative to the connectivity patterns. All reef nodes were found to be highly interconnected with a mean shortest path ranging from 1.96 to 4.06. Reef nodes were then ranked according to their relative importance in regional connectivity based on five connectivity indices. Despite the between-organism and between-index variability in rankings, there were reefs nodes, mostly located offshore and at major straits, which consistently ranked high. We found that the distribution of existing MPAs partially capture some of the regional connectivity functions but there is a spatial mismatch between the primarily coastal MPAs and the high-ranking reef nodes. Furthermore, network partitioning identified subnetworks and dispersal barriers. The existing MPAs were found to be disproportionately distributed to a few subnetworks and that the largest subnetworks do not contain the greatest number of MPAs. Considering these gaps, we suggest expanding the coverage of protected areas especially in underrepresented reef networks to meaningfully capture national-scale connectivity and meet global conservation objectives.

EEGW: AN ENERGY-EFFICIENT GREY WOLF ROUTING PROTOCOL FOR FANETS

Unmanned Aerial Vehicles (UAVs) or flying drones are employing to retrieve data from their respective sources and help to accomplish Flying Ad hoc Networks (FANETs). These wireless networks deal with challenges and difficulties such as power consumption, packet losses, and weak links between the nodes. This is due to the high mobility of nodes, frequent network partitioning, and uncertain flying movement of the flying drones. Consequently, reduce the reliability of data delivery. Moreover, unbalanced energy consumption results in an earlier failure of flying drone and accelerate the decrease of network life. The performance of FANETs depends on the capabilities of energy consumption of each flying drone. They are expected to live for a longer period to manage the cost overhead. Energy-efficient routing is an important factor that helps in improving the lifetime of FANETs. In this research, we propose an Energy-Efficient Grey Wolf (EEGW) routing protocol for FANETs. This protocol is comprised of Grey Wolf Optimizer (GWO) inspired by the leadership hierarchy of grey wolves. It helps in minimizing the energy consumption, packet loss ratio, and aerial transmission loss incurred during transmission.

Contact Ability Based Topology Control for Predictable Delay-tolerant Networks

In predictable delay tolerant networks (PDTNs), the network topology is known a priori or can be predicted over time such as space planet networks and vehicular networks based on public buses or trains. Due to the intermittent connectivity, network partitioning, and long delays in PDTNs, most of the researchers mainly focuses on routing and data access research. However, topology control can improve energy effectiveness and increase the communication capacity, thus how to maintain the dynamic topology of PDTNs becomes crucial. In this paper, a contact ability based topology control method for PDTNs is proposed. First, the contact ability is calculated using our contact ability calculation model, and then the PDTNs is modeled as an undirected weighted contact graph which includes spatial and contact ability information. The topology control problem is defined as constructing a Minimum Spanning Tree(MST) that the contact ability of the MST is maximized. We propose two algorithms based on undirected weighted contact graph to solve the defined problem, and compare them with the latest method in terms of energy cost and contact ability. Extensive simulation experiments demonstrate that the proposed algorithms can guarantee data transmission effectively, and reduce the network energy consumption significantly.