Method For Wireless Sensor Networks Research Articles

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.

A survey on artificial neural network based energy-efficient and robust routing scheme for pollution monitoring in WSNs

Despite the significant limits put on the resources of the sensor nodes, such as storage, processing power, communication range, and energy, the spectrum of applications for wireless sensor networks (WSNs) is expanding. This is the case even though WSNs have severe limitations. The primary goals of a WSN are to reduce the amount of energy used and the amount of time it takes to transmit data to the sink node. When a significant number of nodes are being installed, as is the situation with monitoring industrial pollutants, this becomes an extremely important point of discussion. We present an energy-efficient and resilient routing method for wireless sensor networks (WSNs) dubbed ELDC that is based on artificial neural networks. In this method, the network is trained on a massive data set that encompasses the vast majority of possible situations. This helps the network become more dependable and responsive to its surroundings. In addition to this, it makes use of a technique that is based on groups, with the understanding that the sizes of the groups might vary. This helps to extend the life of the network as a whole. An artificial neural network enables intelligent, efficient, and robust group organization by providing efficient threshold values for the selection of a group’s chief node and a cluster head based on the back propagation technique. These threshold values are provided by an artificial neural network. As a result, the method that we have suggested is very good at saving energy and is able to extend the life of sensor nodes.

An accurate fault location method for wireless sensor network based on random matrix theory

In order to improve the effect of fault location, this paper proposes an accurate fault location method for wireless sensor networks based on random matrix theory. The standard non Hermite matrix is used to extract accurate fault location data. Considering the volatility of the original data, the original random matrix is preprocessed. Based on the real-time sliding time window method, the space-time characteristic data of network faults are determined, and the precise fault location of wireless sensor networks based on random matrix theory is realized.Experimental results show that the false positive rate of the proposed method is only 2%. The average fault location accuracy is as high as 96.4% and the fault location time is only 15.1 s, which shows that the proposed method has a good location effect.

An Improvement of DV-Hop Localization Algorithm Based on Cyclotomic Method in Wireless Sensor Networks

Location information is one of the crucial and essential elements for monitoring data in wireless sensor networks. The distance vector-hop (DV-Hop) localization algorithm is of practical importance in improving its localization performance. To achieve global optimization, a DV-Hop algorithm based on the cyclotomic method and weighted normalization, also known as CMWN-DV-Hop, is nominated in this paper. Therefore, the segmentation and weighting factors are introduced and normalized. The weighted recursive least-squares (WRLS) algorithm is chosen to compute the coordinates of the unknown nodes. The effects of various factors on this algorithm are tested, including the number of nodes, the anchor node ratio, and the communication radius. The simulation results show that the proposed algorithm has a super performance in reducing the localization error.

Data fusion method for wireless sensor network based on machine learning

In order to improve the energy consumption balance between wireless sensor nodes and reduce the energy consumption of nodes in the process of data fusion, a machine learning based data fusion method for wireless sensor networks is proposed. Through the establishment and training of wireless sensor network model, the compressed sensing method is used to collect wireless sensor network data, and the multi-dimensional de aggregation class analysis algorithm is used to de duplicate the collected data. Using the spatial correlation between the data collected by multiple sensor nodes, the DCS method is used to process the abnormal data of WSN network. In order to eliminate the influence of measurement error on the fusion accuracy, the WSN network data is preliminarily fused by combining the adaptive theory with the batch estimation fusion algorithm. Based on the preliminary fusion results of WSN network data, the Bayesian inference method in machine learning algorithm is used to further fuse WSN network data. The experimental results show that the number of surviving nodes is large and the energy consumption is low when using this method for data fusion. The energy consumption between wireless sensor nodes has a certain balance, which proves that this method has a good data fusion effect.

An Anomaly Detection Method for Wireless Sensor Networks Based on the Improved Isolation Forest

With the continuous development of technologies such as the Internet of Things (IoT) and cloud computing, sensors collect and store large amounts of sensory data, realizing real-time recording and perception of the environment. Due to the open characteristics of WSN, the security risks during information transmission are prominent, and network attack or intrusion is likely to occur. Therefore, effective anomaly detection is vital for IoT systems to keep the system safe. The original Isolation Forest algorithm is an anomaly detection algorithm with linear time complexity and has a better detection effect on perceptual data. However, there are also disadvantages such as strong randomness, low generalization performance, and insufficient stability. This paper proposes a data anomaly detection method named BS-iForest (box plot-sampled iForest) for wireless sensor networks based on a variant of Isolation Forest to address the problems. This method first uses the sub-dataset filtered by the box graph to train and construct trees. Then, isolation trees with higher accuracy are selected in the training set to form a base forest anomaly detector. Next, the base forest anomaly detector uses anomaly detection to judge data outliers during the next period. These experiments were performed on datasets collected from sensors deployed in a data center of a university, and the Breast Wisconsin (BreastW) dataset, showing the performance of the variant of the Isolation Forest algorithm. Compared with the traditional isolation forest, the area under the curve (AUC) increased by 1.5% and 7.7%, which verified that the proposed method outperforms the standard Isolation Forest algorithm with the two datasets we chose.

Dynamic key distribution method for wireless sensor networks based on exponential algorithm

Dynamic key distribution method for wireless sensor networks based on exponential algorithm

Study on privacy node encryption method for wireless sensor networks based on edge computing

Study on privacy node encryption method for wireless sensor networks based on edge computing

A fuzzy logic and DEEC protocol-based clustering routing method for wireless sensor networks

<abstract> <p>Power-efficient wireless sensor network routing techniques (WSN). Clustering is used to extend WSNs' lifetimes. One node act as the cluster head (CH) to represent the others in communications. The member nodes are less important than the cluster hub (CH) in the clustering procedure. Fuzzy techniques based on clustering theory may provide evenly distributed loads. In this study, we provide a fuzzy-logic-based solution that factors in distance to base station (BS), number of nodes, remaining energy, compactness, distance to communicate within a cluster, number of CH, and remaining energy. Fuzzy clustering has a preliminary and final step. First, we select CH based on distance to the base station (BS), remaining node vigor, and node compactness. In the second phase, clusters are created by combining nodes that aren't already in a CH, using density, outstanding vigor, and detachment as limitations. The proposed solution increases load balancing and node longevity. This work provides a unique hybrid routing technique for forming clusters and managing data transfer to the base station. Simulation findings confirm the protocol's functionality and competence. Reduced energy use keeps network sensor nodes online longer. The framework outperforms Stable Election Protocol (SEP), hybrid energy-efficient distributed clustering (HEED), and Low Energy Adaptive Clustering Hierarchy (LEACH). Using the nodes' energy levels to create a grid pattern for the clusters gave four clusters. In addition, the proposed method has a 4347%, 41.46%, 39.26%, 37.57% and 35.67% reduction in average energy consumption when compared with the conventional algorithms. The proposed technologies could increase the network's lifetime, stability interval, packet transfer rate (throughput), and average energy. The suggested protocol is at least 50% better in every statistic that was looked at, such as network lifetime, stability interval, packet transmission rate (throughput), and average energy use.</p> </abstract>

Study on three-dimensional coverage method for wireless sensor networks based on greedy algorithm

Study on three-dimensional coverage method for wireless sensor networks based on greedy algorithm

Study on privacy node encryption method for wireless sensor networks based on edge computing

Aiming at the problem of poor encryption effect of the existing wireless sensor network privacy node encryption method, a wireless sensor network privacy node encryption method is designed based on edge computing. Firstly, the wireless sensor network structure and protocol stack structure are clarified, the composition structure of privacy nodes and the set of neighbour nodes are determined, and then the privacy node sensitivity is obtained through the adjacency matrix, and the privacy node location of wireless sensor network is realised by the trilateral positioning method. Finally, the edge computing method is introduced to aggregate the number of edge nodes of privacy nodes, complete the division of privacy node datasets, set terminal encryption keys for them, and realise wireless sensor network privacy node encryption. The experimental results show that the positioning error of this method is up to 0.9%, which can effectively improve the positioning accuracy of privacy nodes and improve the security of the network.

Clock synchronisation method for wireless sensor networks based on phase compensation

Clock synchronisation method for wireless sensor networks based on phase compensation

A Power Allocation Method for Wireless Sensor Networks Based on Data Priority: A Case Study of Scraper Chain Tension Monitoring Network

A Power Allocation Method for Wireless Sensor Networks Based on Data Priority: A Case Study of Scraper Chain Tension Monitoring Network

Study on three dimensional coverage method for wireless sensor networks based on greedy algorithm

study on three dimensional coverage method for wireless sensor networks based on greedy algorithm

Energy Efficient Unequal Fault Tolerance Clustering Approach

For achieving Energy-Efficiency in wireless sensor networks (WSNs), different schemes have been proposed which focuses only on reducing the energy consumption. A shortest path determines for the Base Station (BS), but fault tolerance and energy balancing gives equal importance for improving the network lifetime. For saving energy in WSNs, clustering is considered as one of the effective methods for Wireless Sensor Networks. Because of the excessive overload, more energy consumed by cluster heads (CHs) in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to failure. For increasing the WSNs’ lifetime, the CHs selection has played a key role in energy consumption for sensor nodes. An Energy Efficient Unequal Fault Tolerant Clustering Approach (EEUFTC) is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization (PSO). In this approach, an optimal Master Cluster Head (MCH)-Master data Aggregator (MDA), selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator (MDA), and Master Cluster Head. The data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the BS. Thus, the MCH overhead reduces. During the heavy communication of data, overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA). To describe the proposed method superiority based on various performance metrics, simulation and results are compared to the existing methods.

Security key distribution method of wireless sensor network based on DV-hop algorithm

Security key distribution method of wireless sensor network based on DV-hop algorithm

An energy cooperation method of wireless sensor networks based on partially observable Markov decision processes

The performance of routing protocols in wireless sensor networks, such as energy conservation, energy and load balancing. These indicators have a very important impact on the network lifetime. Energy cooperation technology refers to the purposeful transfer of energy from nodes with sufficient energy to nodes with insufficient energy. Firstly, according to POMDP (Partially Observable Markov Decision Process) model, related theories and solving algorithms, this paper proposes an energy cooperation method based on POMDP, establishes an interference decision model based on POMDP, and solves the model. This paper proposes an energy balance routing algorithm based on multi-attribute decision making (EBR_MDM). This algorithm is used to solve the application problem of wireless sensor networks with stable data flow. Including the uneven distribution of network energy, hot spots and energy holes, packet loss caused by node overload and retransmission energy consumption. According to the relative entropy, the attribute weight is calculated, and the optimal forward data is selected to the neighboring nodes. The simulation results show that the network running time of EBR_MDM is increased by 22.1 %, 48.7 % and 101.3 % respectively compared with other comparison protocols. Finally, because the sensor nodes are randomly arranged in the predetermined monitoring area, and the distribution of each node is chaotic, this paper establishes TMA (Trilateral Measurement Algorithm) algorithm to locate the target. The simulation results show that the positioning error of TMA may be about 1.2 m due to trilateration, but it is still within an acceptable range.