Different Number Of Nodes Research Articles

An Optimal Anchor Placement Method for Localization in Large-Scale Wireless Sensor Networks

Localization is an essential task in Wireless Sensor Networks (WSN) for various use cases such as target tracking and object monitoring. Anchor nodes play a critical role in this task since they can find their location via GPS signals or manual setup mechanisms and help other nodes in the network determine their locations. Therefore, the optimal placement of anchor nodes in a WSN is of particular interest for reducing the energy consumption while yielding better accuracy at finding locations of the nodes. In this paper, we propose a novel approach for finding the optimal number of anchor nodes and an optimal placement strategy of them in a large-scale WSN, based on the output of Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) methods. As an initial step in this approach, the virtual localization process is executed over a virtual coordinate system in order to optimize the efficiency of the localization process. GWO and PSO methods are compared with a coverage-based analytical method and machine learning approaches such as Support Vector Machine (SVM) regression and Multiple Regression. The simulations we run with different numbers of nodes in a WSN and different communication ranges of nodes demonstrate that the proposed approaches are superior for minimizing the localization errors while reducing the number of anchor nodes.

New Multipath OLSR Protocol Version for Heterogeneous Ad Hoc Networks

From a basic refrigerator to a self-driving car, emerging technologies are increasingly involving various facets of our daily lives. These bring together many regularly used devices, each with its own characteristics, to communicate and collaborate within the same system. Computer network experts regard this so-called structure as a heterogeneous network made up of several connected objects that do not speak the same language. Communication is therefore ensured by additional types of nodes, such as gateways or converters. In this case, we can detect an increased complexity and a decreased level of security. And thus, the need to adopt a common slang for these kinds of networks has been brought to life. In this work, we compare two different routing protocols: optimized link-state routing (OLSR) and the multipath heterogeneous ad hoc network OLSR (MHAR-OLSR). The latter is an OLSR extension with new functionalities: nodes identification, paths calculation, paths classification, and paths choice that we designed for heterogeneous ad hoc networks composed of MANET, VANET, and FANET devices; it ensures direct communication between these diverse components. We verify and explain all the elements of our solution using colored Petri nets. We also present a global evaluation of Packet Delivery Ratio (PDR), End-To-End Delay, and energy consumption as QoS measures with different numbers of nodes in a heterogeneous scenario. To do this, we use NS-3 and BonnMotion as a tool-set of simulation. Experimental results show improvement in performance when compared to the classical routing protocol.

A Generating - Absorbing Boundary Condition Applied to Wave - Current Interactions Using the Method of Fundamental Solutions

Abstract The purpose of this work is to study the feasibility and efficiency of Generating Absorbing Boundary Conditions (GABCs), applied to wave-current interactions using the Method of Fundamental Solutions (MFS) as radial basis function, the problem is solved by collocation method. The objective is modeling wave-current interactions phenomena applied in a Numerical Wave Tank (NWT) where the flow is described within the potential theory, using a condition without resorting to the sponge layers on the boundaries. To check the feasibility and efficiency of GABCs presented in this paper, we verify accurately the numerical solutions by comparing the numerical solutions with the analytical ones. Further, we check the accuracy of numerical solutions by trying a different number of nodes. Thereafter, we evaluate the influence of different aspects of current (coplanar current, without current, and opposing current) on the wave properties. As an application, we take into account the generating-absorbing boundary conditions GABCs in a computational domain with a wavy downstream wall to confirm the efficiency of the adopted numerical boundary condition.

TSCH Evaluation under Heterogeneous Mobile Scenarios

Time Slotted Channel Hopping (TSCH) is a medium access protocol defined in the IEEE 802.15.4 standard. It has proven to be one of the most reliable options when it comes to industrial applications. TSCH offers a degree of high flexibility and can be tailored to the requirements of specific applications. Several performance aspects of TSCH have been investigated so far, such as the energy consumption, reliability, scalability and many more. However, mobility in TSCH networks remains an aspect that has not been thoroughly explored. In this paper, we examine how TSCH performs under mobility situations. We define two mobile scenarios: one where autonomous agriculture vehicles move on a predefined trail, and a warehouse logistics scenario, where autonomous robots/vehicles and workers move randomly. We examine how different TSCH scheduling approaches perform on these mobility patterns and when a different number of nodes are operating. The results show that the current TSCH scheduling approaches are not able to handle mobile scenarios efficiently. Moreover, the results provide insights on how TSCH scheduling can be improved for mobile applications.

Automatic construction of the dialog tree based on unmarked text corpora in Russian

In this paper, we propose a method for automatically determining the structure of the tree and the key topics of nodes in the process of building a dialog tree based on unmarked text corpora. Building a dialog tree is one of the time-consuming tasks when creating an automatic dialog system and in most cases is performed on the basis of manual markup, which takes a lot of time and resources. The method of hierarchical clustering of dialogs takes into account the semantic proximity of messages, allows one to allocate a different number of nodes at each level of the hierarchy and limit the dialog tree in width and depth. The algorithm for constructing annotations of nodes of the dialog tree takes into account the hierarchy of topics by building thematic chains. The method is based on the complex use of natural language processing methods (tokenization, lemmatization, part-of-speech tagging, word embeddings, etc.), analysis of the main components to reduce the dimension and methods of cluster analysis. Experiments on constructing the structure of the dialog tree and annotating nodes have shown the great possibilities of the proposed method for constructing an automatic dialog tree. The recognition accuracy on the example of the reference dialog tree containing 13 nodes at the first level, 381 nodes at the second level and 299 nodes at the third level was 0.8, 0.7 and 0.5, respectively. Automatic construction of dialog trees can be in demand when developing automatic dialog systems and for improving the quality of generating answers to user questions.

Distributed Recommendation Algorithm Based on Fuzzy Clustering

In recent years, the Internet is sneaking into every corner of users’ lives at a speed visible to the naked eye. Information has shown a geometric growth with the development of the Internet. This article mainly studies the application of distributed recommendation algorithm based on fuzzy clustering. In this paper, the data set used is divided into training set, test set and validation set in a random manner. The ratio of the three number sets is 7:1:2. This paper randomly selects 70% of consumers’ historical data as the training data set, and the last 10% as the test set. The USCensus 1990 raw data set is divided into three groups, numbered 1, 2, and 3, and the number of samples is 400000, 600000 and 800000 respectively. Under the Hadoop platform, the MrDPCND-FCM algorithm is used to cluster the three sets of data to be tested, the number of nodes is increased from 2 to 5, and the time taken to cluster different data sets to be tested under different numbers of nodes is recorded. Therefore, offline testing uses items that the user has already evaluated. If the user has not scored the item, it cannot be evaluated. When the number of clusters is 60, the data sparsity drops from 92% to 51%. When the number of clusters reaches 5, the sparsity drops to around 3%. The algorithm in this paper solves the problem of large-scale data to a certain extent, and improves the efficiency and accuracy.

Application Layer ARQ Algorithm for Real-Time Multi-Source Data Streaming in UAV Networks.

Because of the specific characteristics of Unmanned Aerial Vehicle (UAV) networks and real-time applications, the trade-off between delay and reliability imposes problems for streaming video. Buffer management and drop packets policies play a critical role in the final quality of the video received by the end station. In this paper, we present a reactive buffer management algorithm, called Multi-Source Application Layer Automatic Repeat Request (MS-AL-ARQ), for a real-time non-interactive video streaming system installed on a standalone UAV network. This algorithm implements a selective-repeat ARQ model for a multi-source download scenario using a shared buffer for packet reordering, packet recovery, and measurement of Quality of Service (QoS) metrics (packet loss rate, delay and, delay jitter). The proposed algorithm MS-AL-ARQ will be injected on the application layer to alleviate packet loss due to wireless interference and collision while the destination node (base station) receives video data in real-time from different transmitters at the same time. Moreover, it will identify and detect packet loss events for each data flow and send Negative-Acknowledgments (NACKs) if packets were lost. Additionally, the one-way packet delay, jitter, and packet loss ratio will be calculated for each data flow to investigate the performances of the algorithm for different numbers of nodes under different network conditions. We show that the presented algorithm improves the QoS of the video data received under the worst network connection conditions. Furthermore, some congestion issues during deep analyses of the algorithm’s performances have been identified and explained.

An on demand load balancing multi-path routing protocol for differentiated services in MWSN

As an important part of the Internet of Things, mobile wireless sensor network (MWSN) will generate traffic with different service quality requirements due to the multi-sensor integration of nodes and application diversity. Due to the topological changes, resource constraints and self-organizing characteristics of MWSN, the Per-Hop Behavior (PHB) approach in the traditional Diff-Serv model has many challenges in providing differential service. In this paper, a path reservation multipath routing (PRMR) protocol is proposed, which can provide a suitable path for each type of traffic with service requirements. PRMR protocol includes path discovery algorithm and packet scheduling algorithm. In addition, the path scheduling model and scheduling algorithm in PRMR solve the problem of load imbalance among reserved paths caused by different types of traffic. In scenarios with different number of nodes, three types of traffic, integrity sensitive data, delay sensitive data and normal data, are used to verify the performance of PRMR differential service and load balancing. Simulation experiments not only compare the quality of service provided by each reserved path in PRMR, but also compare the differential service performance between PRMR and several multipath routing algorithms. Simulation results show that PRMR routing algorithm can guarantee high packet delivery rate and low delay for integrity-sensitive data and delay-sensitive data, respectively. In addition, the simulation results of the average residual energy index show that the protocol can achieve network energy balance.

Regeneration and growth in crowns and rhizome fragments of Japanese knotweed (Reynoutria japonica) and desiccation as a potential control strategy.

Reynoutria japonica (Japanese knotweed) is a problematic invasive plant found in many areas of Europe and North America. Notably, in the UK, the species can cause issues with mortgage acquisition. Control of R. japonica is complicated by its ability to regenerate from small fragments of plant material; however, there remains uncertainty about how much (in terms of mass) rhizome is required for successful regeneration. This study investigated the ability of crowns and rhizomes with different numbers of nodes to regenerate successfully from three sites in the north of England, UK. Two of the sites had been subject to herbicide treatment for two years prior to sampling and the third site had no history of herbicide treatment. No significant differences were observed in regenerated stem diameter, maximum height of stem and maximum growth increments among crowns. All traits measured from the planted crowns were significantly greater than those of the planted rhizome fragments and at least one node was necessary for successful regeneration of rhizomes. The smallest initial fragment weight to regenerate and survive the experiment was 0.5 g. Subjecting all plant material to desiccation for 38 days resulted in no regrowth (emergence or regeneration) after replanting. These findings suggest that desiccation could be a valuable management strategy for small to medium scale infestations common in urban settings.

Under water motion tracking and monitoring using wireless sensor network and Machine learning

Wireless Sensor Networks (WSNs) plays an indispensable role in different application and development in new generation technology. In 5G technology, one of the essential features is the connectivity with registered and unregistered networks to promote remote automation. By which the technology can be harnessed at its best and scaled in multi-dimensional applications. These applications are also enabled for diverse motion detection and object tracking applications. In this presented work, an underwater object tracking technique has been proposed, which enable the prediction of the mobile sensor node. The method involves a simulation which demonstrates a 2D-UASN architecture of underwater scenario and in this network using a different number of nodes using random positioning static sensor nodes are deployed. Finally, a mobile sensor node with random mobility is introduced into the network. The approach follows statistical analysis to compute the next possible position of the node. Based on experiments, the accuracy of the predictive methodology is measured in two different scenarios. In the first scenario, the number of nodes is variable, and the simulation area kept fixed size, i.e., 1000X1000. Additionally, in the second scenario, the number of nodes remains fixed, and the simulation area is variable. The results demonstrate that when we deploy sensor nodes randomly in a large area and network node density is less, the detection accuracy significantly affects and demonstrates low accuracy compared to dense area networks. Secondly, the prediction trend from dense to less dense area demonstrates the reducing accuracy.

TRIPLEPR-MAC: A Triple Queue Priority Based Medium Access Control Protocol for Wireless Sensor Network

Background: WSN is a network of smart tiny electromechanical devices named sensors. Sensors perform various tasks like sensing the environment as per its range, transmitting the data using transmission units, storing the data in the storage unit and performing an action based on the captured data. As they are installed in an unfriendly environment, recharging the sensors is not possible every time which leads to a limited lifetime of the network. To enhance the life of a sensor network, the network requires energy-efficient protocols. Various energy-efficient MAC protocols have been developed by the research community, but very few of them are integrated with the priority-based environment which performs priority-based data transmission. Another challenge of WSN is that most of the WSN areas are delay-sensitive because it is implemented in critical fields like military, disaster management, and health monitoring. Energy, delay, and throughput are major quality factors that affect the sensor network. Objective: In this paper, the aim is to design and develop a MAC Protocol for a field like the military where the system requires energy efficiency and priority-based data transmission. Method: In the proposed model, a cluster-based network with priority queues is formed that can achieve higher power efficiency and less delay for sensitive data. Results: In this research, the simulation of proposed MAC, TMAC and SMAC is done with different numbers of nodes, same inter-packet intervals, and variant inter-packet intervals. Based on the script simulation, result graphs are generated. Conclusion: The proposed work achieves greater lifetime compared to TMAC and SMAC using priority-based data transmission.

Graph Neural Networks: Architectures, Stability, and Transferability

Graph neural networks (GNNs) are information processing architectures for signals supported on graphs. They are presented here as generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters instead of banks of classical convolutional filters. Otherwise, GNNs operate as CNNs. Filters are composed of pointwise nonlinearities and stacked in layers. It is shown that GNN architectures exhibit equivariance to permutation and stability to graph deformations. These properties help explain the good performance of GNNs that can be observed empirically. It is also shown that if graphs converge to a limit object, a graphon, GNNs converge to a corresponding limit object, a graphon neural network. This convergence justifies the transferability of GNNs across networks with different numbers of nodes. Concepts are illustrated by the application of GNNs to recommendation systems, decentralized collaborative control, and wireless communication networks.

AN INVESTIGATION INTO THE PERFORMANCE OF THE MULTILAYER PERCEPTRON ARCHITECTURE OF DEEP LEARNING IN FORECASTING STOCK PRICES

A wide range of studies have been conducted on deep learning to forecast time series data. However, very few researches have discussed the optimal number of hidden layers and nodes in each hidden layer of the architecture. It is crucial to study the number of hidden layers and nodes in each hidden layer as it controls the performance of the architecture. Apart from that, in the presence of the activation function, diverse computation between the hidden layers and output layer can take place. Therefore, in this study, the multilayer perceptron (MLP) architecture is developed using the Python software to forecast time series data. Then, the developed architecture is applied on the Apple Inc. stock price due to its volatile characteristic. Using historical prices, the accuracy of the forecast is measured by the different activation functions, number of hidden layers and size of data. The Keras deep learning library, which can be found in the Python software, is used to develop the MLP architecture to forecast the Apple Inc. stock price. The developed model is then applied on different cases, namely different sizes of data, different activation functions, different numbers of hidden layers of up to nine layers, and different numbers of nodes in each hidden layer. Then, the metrics mean squared error (MSE), mean absolute error (MAE) and root-mean-square error (RMSE) are employed to test the accuracy of the forecast. It is found that the architecture with rectified linear unit (ReLU) outperformed in every hidden layer and each case with the highest accuracy. To conclude, the optimal number of hidden layers differs in every case as there are other influencing factors.

Plant growth prediction model for lettuce (Lactuca sativa.) in plant factories using artificial neural network

One of the applications of precision agriculture is the monitoring of plant growth in a plant factory production to observe the behavior and predict the estimated yield of plant production. Plant growth is unique and is affected by internal and external factors, such as environmental conditions and nutrition supply. The estimation of plant growth considering the environmental conditions as well as initial plant height is necessary for plant management during the production cycle. Therefore, to answer the challenge, the purpose of this study was to develop a model of plant growth prediction using the resilient backpropagation Artificial Neural Network (ANN) method with environmental parameter input at the plant factory and evaluate the model. The ANN model was tested using a different number of nodes at the hidden layer, which are 1 to 7 nodes with the input of daily average temperature, average daily humidity, EC, and light intensity and then produces high lettuce increase output for 45 days. The model was developed and tested using the lettuce (Lactuca sativa) in plant factory production. As a result of the evaluation, the best prediction model with ANN is using the network architecture 4-7-1 with the results of the interpretation of R2 on the training data, and testing data are 0.987 and 0.728. From the verification test of the developed model, it can be found that the most affecting way to optimize lettuce growth is the rate of EC in nutrition. The results of the RMSE model validation is 0.032. Accordingly, the developed model can be used to predict the height increase of Lettuce (Lactuca sativa) plants in a plant factory.

An Efficient Hybrid Multicast Protocol for Manet

Routing protocols for MANET have to face the challenge of frequently changing topologies. Conventional proactive and reactive type routing protocols are less efficient under these circumstances. Hybrid protocol named Zone Routing Protocol clubs the advantages of the former protocols by maintaining an up to date topological map. Several virtual architectures are being used for better robust and scalable membership management without maintenance of state information. A Robust and Scalable Geographic Multicast protocol is proposed in this paper forwards the control messages and data packets without explicitly creating and maintain a tree structure. A predefined and properly organized source tracking mechanism is designed to exclude flooding of the MANET source information. Virtual networks are deployed and analyzed for different number of nodes. Our simulation results and comparison shows that proposed protocol offers higher, throughput, lesser drop and lower control over head with even with varying network sizes, moving speeds and node densities.

An Efficient Hybrid Multicast Protocol for Manet

Routing protocols for MANET have to face the challenge of frequently changing topologies. Conventional proactive and reactive type routing protocols are less efficient under these circumstances. Hybrid protocol named Zone Routing Protocol clubs the advantages of the former protocols by maintaining an up to date topological map. Several virtual architectures are being used for better robust and scalable membership management without maintenance of state information. A Robust and Scalable Geographic Multicast protocol is proposed in this paper forwards the control messages and data packets without explicitly creating and maintain a tree structure. A predefined and properly organized source tracking mechanism is designed to exclude flooding of the MANET source information. Virtual networks are deployed and analyzed for different number of nodes. Our simulation results and comparison shows that proposed protocol offers higher, throughput, lesser drop and lower control over head with even with varying network sizes, moving speeds and node densities.

Speeding up the ODETTA code for solving particle transport problems

Mathematical simulation of fast neutron reactors requires high-precision calculations of protection problems based on unstructured meshes. The paper considers and analyzes a parallel version of the ODETTA code (Belousov et al. 2019) with the use of the MPI (Message Passing Interface) library technology (Knyazeva et al. 2006). The code is designed for numerical simulation of neutronic processes in shielding compositions of fast neutron lead cooled reactor plants in normal operating modes, and can be used to calculate the radiation conditions of using structural components and equipment of nuclear power facilities which are assumed to be the sources of and/or exposed to ionizing radiation during their safety justification. The operation of the generated code is compared against the previous version. The MPI-based development of the ODETTA code’s algorithmic part is described. Peculiarities and specific features of the code parallelization are presented, the code modification is given, and respective algorithms are considered. The structure of the ODETTA code based on the MPI is described in brief. The results of using the ODETTA code’s serial and parallel versions in OS Linux (Kostromin 2012) for NRNU MEPhI’s HPC cluster are provided (Savchenko et al. 2020). A comparative analysis is presented for two code implementation options in terms of speed and accuracy of results when using two different clusters and different numbers of nodes for these. Peculiarities of cluster-based calculations are noted.

Performance Evaluation of Mobility Models over UDP Traffic Pattern for MANET Using NS-2

The current study presents the simulative study and evaluation of MANET mobility models over UDP traffic pattern to determine the effects of this traffic pattern on mobility models in MANET which is implemented in NS-2.35 according to various performance metri (Throughput, AED (Average End-2-end Delay), drop packets, NRL (Normalize Routing Load) and PDF (Packet Delivery Fraction)) with various parameters such as different velocities, different environment areas, different number of nodes, different traffic rates, different traffic sources, different pause times and different simulation times . A routing protocol.…was exploited AODV(Adhoc On demand Distance Vector) and RWP (Random Waypoint), GMM (Gauss Markov Model), RPGM (Reference Point Group Model) and MGM (Manhattan Grid Model) mobility models above CBR traffic sources. The results of Reference Point Group Model simulation illuminate that routing protocol performance is best with RPG mobility model than other models.

An End-to-End Multiplex Graph Neural Network for Graph Representation Learning

Research on graph classification tasks based on graph neural networks has attracted wide attention. The graphs to be classified may have various graph sizes (i.e., different numbers of nodes and edges) and have various graph properties (e.g., average node degree, diameter, and clustering coefficient). The diverse property of graphs has imposed significant challenges on existing graph learning techniques since diverse graphs have different best-fit hyperparameters. Consequently, it is unreasonable to learn graph representation from a set of diverse graphs by a unified graph neural network. Inspired by this, we design an end-to-end Multiplex Graph Neural Network (MxGNN) that learns graph representations with multiple GNNs, and combines them with a learnable method. The main challenge lies with the combination of multiple representation results. Our new findings show that the a priori graph properties do have an effect on the quality of representation learning, which can be used to guide the learning. Our experiments on graph classification with multiple data sets show that the performance of MxGNN is better than the existing graph representation learning methods.

Process Configuration Studies of Methanol Production via Carbon Dioxide Hydrogenation: Process Simulation-Based Optimization Using Artificial Neural Networks

Methanol production via carbon dioxide (CO2) hydrogenation is a green chemical process, which can reduce CO2 emission. The operating conditions for minimum methanol production cost of three configurations were investigated in this work. An artificial neural network with Latin hypercube sampling technique was applied to construct model-represented methanol production. Price sensitivity was performed to study the impacts of the raw materials price on methanol production cost. Price sensitivity results showed that the hydrogen price has a large impact on the methanol production cost. In mathematical modeling using feedforward artificial neural networks, four different numbers of nodes were used to train artificial neural networks. The artificial neural network with eight numbers of nodes showed the most suitable configuration, which yielded the lowest percent error between the actual and predicted methanol production cost. The optimization results showed that the recommended process design among the three studied configurations was the process of methanol production with two reactors in series. The minimum methanol production cost obtained from this configuration was $888.85 per ton produced methanol, which was the lowest methanol production cost among all configurations.