Node Deployment In Wireless Sensor Networks Research Articles

Application of intelligent algorithm in wireless sensor network node deployment optimization

Application of intelligent algorithm in wireless sensor network node deployment optimization

Wireless sensor network deployment optimization for a smart farming application: comparison of two Multi-Objective Evolutionary Algorithms

As it is one of the main means of insuring food security, agriculture has employed different technologies such as the Internet of Things and wireless sensor networks (WSN), to improve the quality and quantity of agricultural products, while preserving natural resources. But unfortunately, in agricultural plots where we have large surfaces of interest, the optimization of node deployment in a WSN remains among the major problems to be solved. In this work, we proposed a WSN node deployment optimization model for an agricultural application according to classical constraints of coverage, over-coverage, connectivity and nodes number, in addition to the nodes separating distance constraint which affects the quality of physical parameters models. We have applied two variants of Multi-Objective Genetic Algorithms, the Non Sorting Genetic Algorithm II (NS-GA II) and the Strength Pareto Evolutionary Algorithm II (SPEA II). As a result, for a 100 m2 plot, both algorithms ensured a communication rate of 100% while SPEA 2 presented a lower sensor number and over-coverage rates with a smaller separating distance, and the execution time of NSGA II was shorter with 11 s. Besides, both of them were greedy in terms of computation time with the increase in the size of the plots.

Research on Optimization of Node Deployment in Wireless Sensor Networks Driven by Artificial Intelligence

The security deployment of WSN (Wireless Sensor Network) nodes also needs to ensure the performance of node coverage, which affects the accuracy and integrity of information detected by the network and the quality of service of the network. It is not enough to improve the network coverage and network performance of nodes only by adjusting the network topology of WSN, so there will be a problem of excessive node density in the network deployment area, which will lead to node redundancy and coverage hole, which will eventually affect the accuracy and integrity of monitoring information. In this paper, the deployment of WSN nodes driven by AI(Artificial Intelligence) is optimized. The data fusion theory is introduced, and the data fusion model is used to integrate node synchronization task scheduling, dynamic node deployment and dynamic network topology recovery.

Application of Artificial Intelligence Technology in the Optimal Deployment of Wireless Sensor Network Nodes

WSN (Wireless Sensor Network) is a network composed of a large number of sensor nodes self-organizing through wireless communication technology. Nodes are prone to failures due to environmental impact and energy depletion, and environmental interference and node failures can easily cause changes in the network topology. This article conducts research on the application of AI (Artificial Intelligence) technology in the optimal deployment of WSN nodes. Using AI technology to optimize the deployment of hybrid WSN mobile nodes, ensuring that the node density of each sub target area reaches its expected node density to ensure effective coverage of the entire target monitoring area. Given the target area range, the number and position of sensor nodes are determined through AI technology, which is the network layout for a given target area. Effectively reducing the data flow of the entire network. Due to the limited energy of sensor nodes, minimizing energy consumption and maximizing network lifespan are the main goals of designing WSN. Deployment strategies must optimize the deployment strategies of sensor nodes, intermediate nodes, and base stations to ensure coverage, connectivity, and robustness.

Quadruple parameter adaptation growth optimizer with integrated distribution, confrontation, and balance features for optimization

Growth optimizer is a novel metaheuristic algorithm that has powerful numerical optimization capabilities. However, its parameters and search operators become crucial factors that significantly impact its optimization capability for engineering problems and benchmarks. Therefore, this paper proposes a quadruple parameter adaptation growth optimizer (QAGO) integrated with distribution, confrontation, and balance features. In QAGO, the quadruple parameter adaptation mechanism aims to reduce the algorithmic sensitivity for parameter setting and enhance the algorithmic adaptability. By employing parameter sampling that adheres to specific probability distributions, the parameter adaptation mechanism achieves dynamic tuning of the algorithm hyperparameters. Moreover, one-dimensional mapping and fitness difference methods are designed in the triple parameter self-adaptation mechanism based on the contradictory relationship to adjust the operator’s parameters. After that, ”spear” and ”shield” are balanced based on the Jensen–Shannon divergence in information theory. Furthermore, the topological structure of the operators is redesigned, and by combining the parameter adaptation mechanism, operator refinement is achieved. Refined operators can effectively utilize different evolutionary information to improve the quality of the solution. The experiment evaluates the performance of QAGO on distinct optimization problems on the CEC 2017 and CEC 2022 test suites. To demonstrate the capability of QAGO in solving real-world applications, it was applied to tackle two specific problems: multilevel threshold image segmentation and wireless sensor network node deployment. The results demonstrated that QAGO delivers highly promising optimization results compared to seventy-one high-performance competing algorithms, including the five IEEE CEC competition winners. The source code of the QAGO algorithm is publicly available at https://github.com/tsingke/QAGO.

A Non-Dominated Sorting Genetic Algorithm Based on Voronoi Diagram for Deployment of Wireless Sensor Networks on 3-D Terrains

The deployment strategy for wireless sensor networks (WSNs) affects the quality of service (QoS). Adopting a reasonable deployment strategy can improve the QoS of WSNs. In this paper, the problem regarding sensor node deployment for WSNs on three-dimensional (3D) terrain is modeled as a multi-objective optimization problem. The coverage rate of the WSNs, their unbalanced energy consumption, and the number of sensor nodes are used as fitness functions for the optimization problem. We propose a non-dominated sorting genetic algorithm based on a Voronoi diagram (VNSGA) for solving the wireless sensor network deployment issue and improving the QoS of WSNs on 3D terrain. The proposed algorithm applies the Voronoi diagram to obtain the node-sensing radius and communication radius, which are suitable for 3D terrain with respect to calculating the fitness function of the optimization problem. The Pareto optimal solution is obtained by retaining the solution close to the reference point. The experiments compare the proposed algorithm with the Multi-Objective Particle Swarm Algorithm (MOPSO) and the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) on two terrains with different ranges. The experimental results show that the proposed algorithm outperforms the comparison algorithm on both terrains with different range sizes. The proposed algorithm can improve the coverage to 97.95% and reduce the imbalance in energy consumption to 9.13% on large range terrain.

Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations.

This paper proposes an integrated method for the estimation of soil moisture in potato crops that uses a low-cost wireless sensor network (WSN). Soil moisture estimation maps were created by applying the Kriging technique over a WSN composed of nodes. Our goal is to estimate the soil moisture of the crop with a small-scale WSN. Using a perfect mesh approach on a potato crop, experimental results demonstrated that 25 WSN nodes were optimal and sufficient for soil moisture characterization, achieving estimations errors . We provide a strategy to select the number of nodes to use in a WSN, to characterize the moisture behavior for spatio-temporal analysis of soil moisture in the crop. Finally, the implementation cost of this strategy is shown, considering the number of nodes and the corresponding margin of error.

Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication

For the past few years, huge interest and dramatic development have been shown for the Internet of Things (IoT) based constrained Wireless sensor network (WSN) to achieve efficient resource utilization and better service delivery. IoT requires a better communication network for data transmission between heterogeneous devices and an optimally deployed energy-efficient WSN. The clustering technique applied for WSN node deployment needs to be efficient; therefore, the entire architecture can obtain a better network lifetime. While clustering, the entire network is partitioned into various clusters. Moreover, the cluster head (CH) selection process also needs proper attention for achieving efficient data communication towards the sink node via selected CH and also for increasing the node reachability within the cluster. In this proposed framework, an energy efficient deep belief network (DBN) based routing protocol is developed, which achieves better data transmission through the selected path. Due to this the packet delivery ratio (PDR) gets improved. In this framework, initially, the nodes in the whole network is grouped as clusters using a reinforcement learning (RL) algorithm, which assigns a reward for the nodes that belong to the particular cluster. Then, the CH required for efficient data communication is selected using a Mantaray Foraging Optimization (MRFO) algorithm. The data is transmitted to the sink node via the selected CH using an efficient deep learning approach. At last, the performance of proposed deep network based routing protocol is evaluated using different evaluation metrics they are network lifetime, energy consumption, number of alive nodes, and packet delivery rate. Finally, the evaluated results are compared with few existing algorithms. Among all these algorithms, the proposed DBN routing protocol has achieved better network lifetime.

Construction of node deployment model for wireless sensor network based on fuzzy data fusion

With the goal of increasing the coverage of wireless sensor network nodes and reducing node deployment time, a model of wireless sensor network node deployment based on fuzzy data fusion is designed. According to the wireless sensor network structure, the communication range and communication radius of the node are determined, so as to obtain the coverage of the node. Establishing a wireless sensor network coverage perception model to obtain the perception probability and joint perception probability of the target point. Finally, through fuzzy data fusion and fuzzy fusion rules, the fusion of the target node is realized, and the judgment criterion is obtained, and the deployment of the wireless sensor network node is finally completed under constraints. Experimental results show that this method effectively improves node coverage and reduces node deployment time, indicating that this method is beneficial to enhance the perception quality of wireless sensor networks.

WSN Deployment Strategy for Real 3D Terrain Coverage Based on Greedy Algorithm with DEM Probability Coverage Model

The key to the study of node deployment in Wireless Sensor Networks (WSN) is to find the appropriate location of the WSN nodes and reduce the cost of network deployment while meeting the monitoring requirements in the covered area. This paper proposes a WSN node deployment algorithm based on real 3D terrain, which provides an effective solution to the surface-covering problem. First of all, actual geographic elevation data is adopted to conduct surface modeling. The model can vividly reflect the real terrain characteristics of the area to be deployed and make the deployment plan more visible and easy to adjust. Secondly, a probabilistic coverage model based on DEM (Digital Elevation Model) data is proposed. Based on the traditional spherical coverage model, the influence of signal attenuation and terrain occlusion on the coverage model is added to make the deployment model closer to reality. Finally, the Greedy algorithm based on grid scanning is used to deploy nodes. Simulation results show that the proposed algorithm can effectively improve the coverage rate, reduce the deployment cost, and reduce the time and space complexity in solving the WSN node deployment problem under the complex 3D land surface model, which verifies the effectiveness of the proposed algorithm.

Research on Wireless Sensor Network Coverage Path Optimization Based on Biogeography-Based Optimization Algorithm

Path selection is one of the key technologies of wireless sensor network (WSN). A reasonable choice of coverage path can improve the service quality of WSN and extend the life cycle of WSN. Biogeography-based optimization (BBO) is widely used in the field of cluster intelligent optimization because its search method has a better incentive mechanism for population evolution. In this paper, the move-in and move-out operation and mutation operation of the BBO algorithm enable WSN to find an efficient routing path. In this paper, simulation experiments are carried out in two scenarios of regular deployment and random deployment of WSN nodes. The experimental results show that the quality of the WSN coverage path solution optimized by the BBO algorithm in the two scenarios is better than that of the particle swarm algorithm and genetic algorithm.

Wireless Sensor Network Node Deployment Based On Regular Tetrahedron

The deployment of nodes in 3D wireless sensor networks has always been the focus of research. In this paper, two algorithms for achieving full coverage are proposed, which are applicable to the even distribution and the uneven distribution of target points. Among them, the characteristics of the spherical and regular tetrahedrons are rationally utilized to achieve the purpose of covering more space with the same nodes. For the three-dimensional space with uneven target distribution, it is proposed to use the DBSCAN clustering method for target point processing, and then perform the same processing in each cluster to avoid wasting more sensor nodes and increasing energy consumption. Finally, considering the problem of energy saving, some nodes are put to sleep and waiting for the next call to reduce energy consumption and extend the working time of the network. Simulation experiments show that the algorithm has improved the number of nodes used and energy consumption.

Analysis of node deployment in wireless sensor networks in warehouse environment monitoring systems

This paper mainly studies the deployment of wireless sensor network nodes in the warehouse environment monitoring system, discusses the deployment algorithm of wireless sensor network nodes in the warehouse environment, and finds out the node deployment scheme with better network performance through comparison. Wireless sensor network node deployment is the basis of wireless sensor network application in storage environment monitoring system. It affects the performance of the whole network and is the primary problem to be solved in network application. This paper discusses the advantages of wireless sensor network in the monitoring of storage environment, especially the deployment and simulation analysis of sensor nodes in the warehouse environment. Aiming at the influence of sensor perception model on the effectiveness of the node deployment plan, this paper proposes a node deployment collaborative perception model based on 0-1 perception model and exponential model. The sensor node deployment problem is transformed into a three-dimensional node deployment problem. Finally, the algorithm is applied to tobacco storage environment. In order to verify the effectiveness of the proposed algorithm, the scheme obtained by the proposed algorithm is compared with that obtained by the corresponding deployment algorithm in this paper. The comparison results show that the overall performance of the algorithm is better than that of the usual scheme.

A Hybrid Optimization from Two Virtual Physical Force Algorithms for Dynamic Node Deployment in WSN Applications.

With the rapid development of unmanned aerial vehicle in space exploration and national defense, large-scale wireless sensor network (WSN) became an important and effective technology. It may require highly accurate locating for the nodes in some real applications. The dynamic node topology control of a large-scale WSN in an unmanned region becomes a hot research topic recently, which helps improve the system connectivity and coverage. In this paper, a hybrid optimization based on two different virtual force algorithms inspired by the interactions among physical sensor nodes is proposed to address the self-consistent node deployment in a large-scale WSN. At the early stage, the deployment algorithm was to deploy the sensor nodes by leveraging the particle motions in dusty plasma to achieve the hexagonal topology of the so-called “Yukawa crystal”. After that, another virtual exchange force model was combined to present a hybrid optimization, which could yield perfect hexagonal topology, better network uniformity, higher coverage rate, and faster convergence speed. The influence of node position, velocity, and acceleration during the node deployment stage on the final network topology are carefully discussed for this scheme. It can aid engineers to control the network topology for a large number of wireless sensors with affordable system cost by choosing suitable parameters based on physical environments or application scenarios in the near future.

Multi-criteria based zone head selection in Internet of Things based wireless sensor networks

The past few years have seen dramatic development and a great interest in efficient service delivery and better resource utilization in the Internet of Things (IoT) based constrained Wireless Sensor Network (WSN). The IoT is mainly dependent on optimal deployment of energy aware WSN and efficient communication architecture for data transfer among heterogeneous devices. In addition, energy efficient clustering techniques for WSN node deployment and routing have achieved great involvement for prolonging network lifetime. In clustering technique, where the network is partitioned into different segments (clusters or zones) and proper attention must be given to the cluster head (CH) selection procedure for maximizing node reachability inside the cluster and efficient communication to the base station. In this paper, we have proposed multi-criteria based cluster head/zone head selection scheme in Internet of Things based WSN by considering distinct parameters affecting node energy and network lifetime. These parameters; energy level, distance from neighboring nodes, distance from center of the zone, number of times a node has been zone head and whether a node is merged or not, have direct impact on overall performance of WSN. The relative impact of each parameter in CH/ZH selection is computed using the Analytical Network Process (ANP) which is widely used multi-criteria decision tool. Simulation results of the proposed scheme show relatively better performance than existing energy efficient clustering techniques. The obtained results have been analyzed by varying the number of parameters in ZH selection and their impact on network stability and lifetime.

Wireless sensor network node deployment based on multi-objective immune algorithm

Wireless sensor network node deployment based on multi-objective immune algorithm

Wireless sensor network node deployment based on multi-objective immune algorithm

Wireless sensor network (WSN) is made up of a large number of low-cost wireless sensor nodes, which can collect all types of data in its lifetime. Node deployment of WSN is a NP complete problem, and it can significantly influence the network coverage and energy consumption in WSN. In this paper, we try to exploit multi-objective immune algorithm to solve the node deployment problem in WSN. The proposed node deployment algorithm aims to maximise the degree of network coverage and minimise the energy consumption. In the proposed multi-objective immune algorithm, each antibody refers to a candidate solution in node deployment process, and the antibodies are randomly initialised with a specific range. In addition, we design a fitness function by two ranking modes. Finally, experiments are conducted to test the performance of the proposed algorithm. Particularly, we use some typical performance metric in this experiment, such as coverage degree, energy consumption, and maximum moved distance. Experimental results demonstrate that the proposed can both enhance the network coverage degree and reduce the energy consumption by minimising the moved distance.

A Novel Sensor Node Deployment using Low Discrepancy Sequences for WSN

The foremost challenge in designing Wireless Sensor Networks (WSNs) is careful node placement. Sensor Node placement is a one of the powerful optimization technique for accomplishing the anticipated goals. This paper focuses on categories of node deployment in WSN. The paper highlights various problems and identifies the different objectives in sensor node deployment. The paper proposes a novel node deployment strategy based on Quasi- random method of low-discrepancy sequences to increase the lifetime and the coverage of the network. The aim of the paper is to study how the node deployment affects the different QoS parameters such as packet delivery ratio, average energy consumption, delay, etc. with various multi-objective routing algorithms WSN. To validate the proposal simulation, results are presented in this paper. The paper concludes with the future outlook.

Method of Wireless Sensor Network Node Deployment for the Emergency Environment

Incidents could not always be avoided, resulting in significant losses for the state property and people's safety. Effective monitoring of incidents is becoming more and more important. Wireless sensor networks (WSNs) are widely used in environmental monitoring. On the basis of the existing wireless sensor network node deployment model, a network node deployment model was proposed, which was used in the monitoring of unexpected accidents. The model area was divided into the core area and the evacuation area, and different monitoring nodes deployment plans were applied in different areas. The simulation experiment is carried out by MATLAB, and the simulation results of the node coverage are obtained. Results show that the model for the practical application of the node deployment provides an effective deployment plan, as well as offers a strong basis for real-time monitoring and post-accident emergency evacuation.

Empirical Modeling and Evaluation of Multi-Path Radio Channels on Wheat Farmland Based on Communication Quality

<abstract> Wheat is one of the main food crops in northern China, but the process of wheat production lacks accurate and effective control. Wireless sensor networks (WSN) can be used to monitor environmental information and enable appropriate decision-making for a well-controlled production system. The farmland environment varies with crop growth, which can affect the propagation of electromagnetic signals. To predict the path loss of WSN signals and guide WSN node deployment and topology control, a multi-scale modeling approach is proposed for radio channels on wheat farmland. The environmental factors are extracted based on the relationship between crop growth and signal propagation path. According to different crop sheltering conditions, a logarithmic model and a statistical distribution model are adopted. An empirical model of multi-path channel fading is established and evaluated based on experimental data. The results show that the average correlation coefficient of the large-scale model is >0.95 under the non-sheltering conditions. On the other hand, the mean variation of the multi-scale probability distribution model is <0.1 under the sheltering conditions. The results indicate that an accurate description of multi-path radio channels on wheat farmland is achieved.