The increasing annual demand necessitates an efficient model for planning large-scale networks, which typically include numerous nodes and edges. Designing an operational pipeline that efficiently utilizes resources presents significant challenges. This study presents a graph-based approach for designing a water pipeline network, with graph edges representing pipelines and nodes representing users (demand) or reservoirs. To address this complexity, we use Graph Embedding approach, a machine-learning technique that compresses large-scale graphs into lower-dimensional vector spaces. This method employ a random walk model to make sequence of nodes with their neighborhoods and a skip-gram model train them to ensure accurate modeling of the shortest paths between them. Then, the nodes are clustered in vector space and optimized using the minimum spanning tree(MST) method. The graph generated still preserves essential network characteristics such as node relationships and spatial proximity. The resulting model produces a graph based on the geographic coordinates of nodes that map the layout of the water pipeline network. In forming large-scale network layouts, the graph embedding approach improves the computation time efficiency. We evaluate the model’s performance using the GFS-Score and validate the network layout with EPANET. This approach offers significant potential in advancing the development of intelligent water network structures for large-scale complex networks.
Message routing protocol is a key factor in delivering messages efficiently in Delay/Disruption-tolerant networks (DTNs). Messages sent by a mobile node are transferred in a Store-Carry-Forward manner among mobile nodes in a DTN. Existing protocols take into account mobile nodes’ behavior, history information, and movement model to optimize their mechanisms for exchanging messages while encountering one another. However, none of them consider the construction of the underlying environment, such as road network construction. We propose CD-PRoPHET, a variant of PRoPHET, to tune the delivery probability by considering the road network density. Our simulation results show that CD-PRoPHET significantly reduces the overhead ratio at the cost of the delivery ratio.
IoT is increasingly being used in different areas of society. IoT is used in society by collecting values acquired by sensor devices through networks and analysing the data. IoT software is needed to use the IoT, but efficient software development methods are desired because software for an increasingly complex society tends to become more complex. This research aims to improve the efficiency of IoT software and develop an intuitive method for developing IoT software.This paper proposes a development methodology that combines a data-flow-based programming approach with a procedural programming language. When the focus is on data, data flow programming is intuitive and efficient. On the other hand, when the focus is on data processing, it is better to use a procedural programming language because data is processed sequentially[1].In the hybrid IoT programming environment proposed in this paper, the code of a procedural programming language is generated from the description by data flow programming, and the program is executed by a micro controller.The products of this research are available as open source software[2], [3].
Employee training is expensive and time-consuming as evidenced by the fact that teaching new employees how to operate factory equipment is usually done in groups to save time and resources. For some industrial facilities, training often requires shutting down operations and putting employees and trainees in potentially dangerous situations. Flour mill operators are typically exposed to work hazards such as dust, excessive noise, explosions, electrocutions, and injuries from improperly guarded machinery. Thus, this paper addressed the issues previously mentioned by developing a virtual reality training software mainly focused on the procedures involved during break release measurement. It is the process wherein a mill operator takes samples from specific roller mills and measures the percentage of flour extracted from a flour milling machine. With this software, the flour mill machine is modeled under simulated conditions giving the user a fully immersive training experience without the fear or danger of experiencing real-life errors and accidents. Twenty-five flour mill operators participated in testing and evaluating the VR trainer software. The results indicated that 92% were satisfied with their training experience, 88% were pleased with the software's functionality, and 98.67% gave positive ratings in the user satisfaction category of the VR training environment.
The proliferation of diverse network technologies has enabled concurrent utilization of multiple network media, such as WiFi and 4G/5G links on modern smartphones. While this advancement facilitates simultaneous downloading of varied network contents, traditional multi-connection management methods, like reception-driven requesting schemes, often struggle to adapt to dynamic network conditions. This paper introduces a novel timer-driven requesting scheme applied to both parallel TCP and parallel MPTCP (Multipath TCP) methods, addressing the limitations of existing approaches. Unlike previous studies that relied solely on simulations, we present an experimental evaluation of this scheme on real machines. Our extensive experimentation reveals significant advantages of the timer-driven requesting scheme, particularly its superior adaptability in fluctuating network environments. These findings not only demonstrate the scheme’s practical viability but also underscore its potential to revolutionize content retrieval in multi-network scenarios, paving the way for more efficient and resilient data transfer mechanisms in increasingly complex network ecosystems.
