Method For Wireless Sensor Networks Research Articles

Context Privacy Preservation for User Validation by Wireless Sensors in the Industrial Metaverse Access System

The Industrial Metaverse provides unparalleled prospects for increasing productivity and efficiency across multiple sectors. As wireless sensor networks play an important role in data collection and transmission within this ecosystem, preserving context privacy becomes critical to protecting sensitive information. This paper investigates the issue of context privacy preservation for user validation via AccesSensor in the Industrial Metaverse and presents a technological method to address it. We explore the need for context privacy, look at existing privacy preservation solutions, and propose novel user validation methods that are customized to the Industrial Metaverse’s access system. This method is evaluated on time-based efficiency, privacy method and bandwidth utilization. Our method performs better as compared to the DPSensor. Our research seeks to provide insights and recommendations for developing strong privacy protection methods in wireless sensor networks that operate within the Industrial Metaverse ecosystem.

A Distributed Deployment Method for Wireless Sensor Networks Based on Multi Agent Systems

This paper proposes a distributed deployment method for wireless sensor networks based on multi-agent systems, which addresses the issues of poor self adaptive deployment capability and high deployment costs caused by numerous and diverse types of nodes in the deployment process of wireless sensor networks, leading to blind spots in coverage and redundant nodes. Due to the characteristics of intelligent perception, intelligent communication, and clustering of nodes in wireless sensor network, sensor nodes can be seen as sensing agents, and sensor networks can be seen as distributed multi-agent systems composed of numerous sensing agents. Through the sensing agents' perception of the surrounding environment and communication interaction, a unified deployment model and distributed deployment algorithm with multiple nodes (cluster head nodes) as leaders are established, achieving effective integration of autonomous and leadership mechanisms, distributed management, and centralized management. Taking the problem of sensor network coverage as an example, simulation experiments were conducted, including 12 sensor agents and 1 20 * 20 monitoring areas, to verify how the sensor agents perceive the environment and optimize the migration and deployment process under the coordination of cluster head nodes. The experimental results confirmed the effectiveness of the above models and algorithms. The research results provide a theoretical model and practical approach for the distributed deployment and scheduling of sensor nodes in wireless sensor networks, and can be extended to intelligent Internet of Things systems, such as urban intelligent agent systems, logistics intelligent agent systems, etc., to improve the intelligence of wireless sensor networks deployment and scheduling.

Machine learning‐based data fusion method for wireless sensor networks

AbstractFor wireless sensor networks (WSNs), sensor nodes lose a certain amount of energy during the information collection and transmission process, and sensor nodes powered by non‐replaceable batteries have limited energy and need to be controlled for energy consumption. In the face of the energy consumption issue in WSN data transmission, research has been conducted to analyze data fusion methods in order to reduce energy consumption. Based on machine learning techniques, a Deep Stacked Auto‐Encoder (DSAE) model is constructed and trained using a layer‐wise greedy approach. By combining this model with WSN, an algorithm based on the DSAE model, called Deep Stacked Auto‐Encoder Data Fusion Algorithm (DSAEDFA), is obtained to do data fusion. The results show that compared to other algorithms, the proposed fusion algorithm has better fusion performance. When the number of iterations is set to 500, the DSAEDFA has 281 surviving nodes, which is 10 more than the Back‐Propagation Data Fusion Algorithm (BPDFA) and 144 more than the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm. When the number of failed nodes is 40, the DSAEDFA has a network survival time of 2562 rounds, which is 746 rounds longer than the LEACH algorithm. The research method effectively extends the lifespan of wireless sensor networks and reduces data transmission energy consumption. Compared to previous methods, the proposed method consider the factors of node residual energy and distance on the basis of traditional routing protocols, making the selection of cluster heads more reasonable. The proposed method can organically combine the DSAE model with the clustering model, optimize the data fusion method, and improve the performance of the algorithm. In addition, by combining the DSAE model, a machine learning technique with clustering models has been expanded in terms of the application scope.

Data transmission path planning method for wireless sensor network in grounding grid area based on MM‐DPS hybrid algorithm

AbstractAt present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption.

Clock synchronisation method for wireless sensor networks based on phase compensation

Clock synchronisation method for wireless sensor networks based on phase compensation

A coverage enhancement method for wireless sensor networks based on firefly algorithm

A coverage enhancement method for wireless sensor networks based on firefly algorithm

Reducing Energy Consumption and Clustering in Wireless Sensor Networks using an Improved Discrete Gorilla Troops Optimization Algorithm with Fuzzy Rules

Among the most common and extensively operated methods in wireless sensor networks (WSN) to increase the efficiency and performance of WSN is network clustering because, through this solution, data is transmitted through the closest possible path. In the clustering method, a portion of the nodes in the network become cluster heads, then a cluster is formed by joining the nodes close to the cluster head. However, the main and important problem in this issue is preventing the creation of unbalanced clusters since the spreading of cluster heads in the network can be unequal. In this paper, we have presented an algorithm based on the discrete Gorilla Troops Optimizer (DGTOA) algorithm and K-Means with a fuzzy clustering approach. In this model, first, several cluster heads are chosen by applying the discrete DGTOA algorithm, and then the output of the DGTOA algorithm is given as initial points and coordinates in K-means. Finally, the cluster head is utilized to wield the fuzzy system. Also, different criteria and graphs were used to compare the proposed model, and the obtained results were measured with other methods, and the obtained outcomes indicate the high performance of the proposed model.

An Energy Efficient Cluster Head Selection in WSN Based on Enhanced Chicken Swarm Optimization

Clustering of data are ab effective energy saving method for wireless sensor networks (WSN). However, they incur additional costs in data collections from sensor nodes and sending them to base station (BS) after aggregations. Cluster heads (CHs) in hierarchical cluster-based WSNs use more energy. Therefore, choosing a suitable CHs is essential to conserve energy for sensor nodes and prolong the life of the WSN. Provide energy-saving cluster head selection techniques in this work. where enhanced fuzzy means clustering is used to perform the first cluster construction. Enhanced Chick Swarm optimization (ECSO) selects CHs which is evaluated strictly for varying WSN scenarios and counts of CHs and nodes for values of remaining energies of sensor nodes, sink distances, and distances within clusters. To demonstrate that the proposed technique is superior, the results are compared with those of several other currently used algorithms.

Two-way differential strategy for wireless sensor networks

In this paper, a novel optimal two-way amplify and forward (AF) differential beamforming method for wireless sensor network is proposed. The proposed method is an advanced signal processing technique used to enhance the performance and reliability of the communication link by exploiting the diversity provided by multiple antennas. Unlike current state-of-the-art methods which require the knowledge of channel state information (CSI) at both transmitting and receiving antennas or at least at the receiving antennas, the suggested method does not need CSI at any transmitting or receiving antenna. Moreover, the proposed method enjoys high error performance with high diversity and coding gain and has a very low encoding and decoding complexity. Through our simulations, the proposed method is proved to outperform the best known non-coherent multi-antenna methods.

A Novel Modified Energy and Throughput Efficient LOYAL UN-Supervised LEACH Method for Wireless Sensor Networks

Data Sensing Devices (DSD’s) have gained lot of traction for various use cases like border control, vehicle tracking. Data Sensing device network (DSDN) is shaped with the aid of combining lot of DSD’s across a random area. Like this multiple groups are formed. In each of the group the specific DSD is elected which is responsible for communication between two independent groups. Each of the group head has multiple attributes with first attribute based on distance, the second attribute based on remaining energy. These attributes will be input for the group head selection based on machine learning, The entire DSD’s inside a group are classified into HIGH, MEDIUM and LOW. The first priority will be given to HIGH followed by others for the primary group head selection. LEACH is a classical method used for transmission of chunks to the control center in a DSDN network. The selection of head DSD by LEACH will happen by making use of the random selection of DSD in each group using random probability selection mechanism. During the data chunk deliver the scanning process will happen from the initiator DSD to head DSD and from there the link is established with the base station (BS), the BS will then scan each group until the destination DSD is reached. The selection of head DSD by LEACH causes more holes in the DSDN because there are chances that the non-performer DSD can become a head DSD. Secondly for the transmission of chunks there is lot of back-and-forth propagation between the BS and the normal DSDs which reduces the battery level of the DSD by a large amount. The Energy based LEACH is modified on top of LEACH by measuring the energy of the DSDs and then selecting the group heads but suffers from multiple group head maintenance as well as more number of links. The proposed method will improve this by reducing the links used for end-to-end communication. In the proposed system the communication will happen based on initiator DSD, primary DSDs in different groups and then destination DSD which will avoid overhead compared to existing methods namely E-LEACH and LEACH. The proposed method is compared with LEACH and E-LEACH with respect to time taken, link count, energy consumption, residual energy measure, lifetime and overhead.

Multi-theme hierarchical monitoring method for wireless sensor networks

Multi-theme hierarchical monitoring method for wireless sensor networks

A Novel Buffer Packet Delivery Strategy for High Throughput and Better Health (HTBH) Method in Wireless Sensor Networks

There is massive call for the Packet Sender Device Network (PSDN) primarily based on tracking of areas, figuring out the consequences of climate, detection of enemy vehicles. The PSDN could have useful Packet Sender Devices (PSD’s) which can study the vicinity and then send the data from initial to receiver PSD. There are numerous constraints which have restrictions on the feature like battery, memory, and range. There is hierarchical community wherein the PSDs are spread on more than one area with every vicinity having their very own PSD’s whilst communique has to manifest among PSDs of various areas then it requires chief PSD in every vicinity which have to be elected primarily based on higher battery degree, distance to base station in addition to mobility of the PSD over a duration of time. LEACH suffers from side-to-side propagation among base station and PSDs. LEACH elects the pinnacle PSD primarily based on random possibility which has consequences of Good-Bad Ratio due to the fact there are possibilities that the PSD with low battery degree be selected as a head PSD. The proposed (HTBH) method will select the head PSD based on battery level, distance between base station and PSD, the mobility of PSD. In order to deliver the data to the processing lab PSDs of the area along with head PSD are used. The packets are classified into high and low priority so that preferences can be used during the data delivery process. Along with sending the current data packets even the data packets reside within the PSD which have to send towards the processing centre. Two policies namely single threshold and dual threshold are used to evict the packets within the PSD and then moving them towards processing centre. This study proposes a higher choice of head PSD with the aid of using considering computation of element which takes into consideration battery, distance, and mobility. The communique among the detection vicinity to receiver vicinity takes place with the assist of PSD’s and head PSD there with the aid of using decreasing range of hops. HTBH method is compared to LEACH and E-LEACH with respect various performance parameters.

A dynamic and multi-level key management method in wireless sensor networks (WSNs)

Wireless sensor networks include a set of ultralight sensor nodes with limited energy, low storage capacity, and constrained processing power. Security is a challenging issue in these networks. One approach to maintain security in these networks is key management. When designing key management schemes, the main challenge is to achieve the acceptable security level and manage resources, especially energy. Today, many key management methods use authentication mechanisms. However, these methods are faced with different challenges due to the limited computational capability of nodes and high energy consumption in the network. In this paper, we present a dynamic and multi-level key management method for homogeneous wireless sensor networks. The proposed key management approach uses an authentication mechanism based on message authentication code (MAC), which is calculated by the shared symmetric keys between the transmitter and receiver nodes. In our scheme, the network is divided into five levels. Then, the clustering process is performed at each level, so that the size of the clusters varies in different levels. The proposed approach includes four phases: (1) Network leveling phase (2) Clustering phase (3) Key establishment phase (cluster key, pairwise key, and gateway key) (4) Rekeying phase. In the proposed method, a symmetric encryption algorithm called RC5 is used for producing keys. Our method contributes to the state of the art in wireless sensor networks by proposing a dynamic and multi-level key management approach. The use of a MAC-based authentication mechanism and the division of the network into different levels and clusters allows for efficient key establishment and rekeying, while minimizing energy consumption and communication overhead. Our proposed method is implemented using the NS2 simulator. Then, the simulation results are compared with SKWN and KMP methods. The results show that our proposed method outperforms SKWN and KMP in terms of average energy consumption, required memory, and communication overhead. However, its computational overhead is slightly more than SKWN and KMP.

LPCHS: Linear programming based cluster head selection method in wireless sensor networks

LPCHS: Linear programming based cluster head selection method in wireless sensor networks

A novel distance vector hop localization method for wireless sensor networks

Abstract Wireless sensor networks (WSNs) require accurate localization of sensor nodes for various applications. In this article, we propose the distance vector hop localization method (DVHLM) to address the node dislocation issue in real-time networks. The proposed method combines trilateration and Particle Swarm Optimization techniques to estimate the location of unknown or dislocated nodes. Our methodology includes four steps: coordinate calculation, distance calculation, unknown node position estimation, and estimation correction. To evaluate the proposed method, we conducted simulation experiments and compared its performance with state-of-the-art methods in terms of localization accuracy with known nodes, dislocated nodes, and shadowing effects. Our results demonstrate that DVHLM outperforms the existing methods and achieves better localization accuracy with reduced error. This article provides a valuable contribution to the field of WSNs by proposing a new method with a detailed methodology and superior performance.

Effective cluster scheduling scheme using local gravitation method for wireless sensor networks

Ensuring a certain clustering of distributed sensor nodes is significant for energy efficiency of wireless sensor networks (WSNs). Recently, several energy efficient artificial intelligence based-methods that provide cluster scheduling schemes have been proposed. However, there are few methods providing balanced energy exhausting of the number of nodes in clusters. In this paper, we present a novel cluster scheduling scheme for WSNs using local gravitational method to adaptively determine the number of time-varying clusters based on a sequence of vectors. The main idea in this method is to be inspired by the physical features of gravity force among the mass units. These features are considered to be units of mass of nodes in vector space. It is based on the fact that with clustering, the highest gravitational force on moving the units of mass is applied by the area with feature vectors that are high-density. Cooperative and distributed clustering is formed and the number of clusters is found by sharing the gravitational force between neighboring nodes through a distribution-optimization scheme. The results of this study have been compared with the existing methods in various scenarios and with many performance criteria through the Matlab programming. Especially in dense networks, the proposed algorithm has increased the number of clusters and ensured the clustering balance as much as possible. The proposed method has scaled down the energy consumption, and enhanced the network lifetime.

Angle Residual-Based NLOS Suppression and Localization Method in Wireless Sensor Networks

Angle Residual-Based NLOS Suppression and Localization Method in Wireless Sensor Networks

IoT-based multi-channel information integration method for wireless sensor networks

The speedy progress of the Internet of Things has connected a huge network of smart devices in series and also intensified the pressure of multi-channel information transmission in wireless sensor networks. To solve the transmission of large amount of information, a multi-channel information integration method based on Radial Basis Function neural network, Particle Swarm Optimization algorithm and Dempster-Shafer evidence theory is studied. The method designs an improved counter propagation Radial Basis Function neural network prediction algorithm to correct the abnormal data based on the normal and abnormal values detected by the Grubbs criterion. And then it uses the Dempster-Shafer evidence inference method for information integration on the basis of the complete monitored values after correction obtained using the above method. The lab result indicated that mean absolute error value of the improved counter propagation Radial Basis Function model for anomaly data prediction findings was 4.399, mean square error was 26.820, and root mean squared error value was 5.180. The average time delay of information integration of this research method was 0.24 μs. Compared with other methods, this method has the shortest integration time delay and the highest integration accuracy. The study provides a new method in the multi-channel information integration for wireless sensor networks in the context of internet of things.

3-D Hybrid RSS-AoA Passive Source Localization With Unknown Path Loss Exponent

The use of Received Signal Strength (RSS) mea- surements for passive localization is a simple and versatile method in Wireless Sensor Networks (WSNs). Another commonly used method is based on Angle of Arrival (AoA) measure- ment. Combining these two methods (RSS-AoA) can enhance the reliability of localization. In this letter, we investigate the localization problem (using joint RSS and AoA measurements) when the Path Loss Exponent (PLE) is unknown and only some of the nodes are anchors with known locations. When PLE is unknown, localization performance is affected. Therefore, we propose a joint estimation method for source location and PLE using an iterative Weighted Least Square (WLS) estimation. Simulation results show that our proposed method outperforms other competing algorithms in terms of localization accuracy.

A Jamming Attacks Detection Approach Based on CNN based Quantum Leap Method for Wireless Sensor Network

The wireless sensor network is the most significant largest communication device. WSN has been interfacing with various wireless applications. Because the wireless application needs faster communication and less interruption, the main problem of jamming attacks on wireless networks is that jamming attack detection using various machine learning methods has been used. The reasons for jamming detection may be user behaviour-based and network traffic and energy consumption. The previous machine learning system could not present the jamming attack detection accuracy because the feature selection model of Chi-Squared didn’t perform well for jamming attack detections which determined takes a large dataset to be classified to find the high accuracy for jamming attack detection. To resolve this problem, propose a CNN-based quantum leap method that detects high accuracy for jamming attack detections the WSN-DS dataset collected by the Kaggle repository. Pre-processing using the Z-score Normalization technique will be applied, performing data deviations and assessments from the dataset, and collecting data and checking or evaluating data. Fisher’s Score is used to select the optimal feature of a jamming attack. Finally, the proposed CNN-based quantum leap is used to classify the jamming attacks. The CNN-based quantum leap simulation shows the output for jamming attacks with high precision, high detection, and low false alarm detection.