Data In Wireless Sensor Networks Research Articles

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

The Internet of Things (IoT) and wireless sensor networks (WSNs) have evolved rapidly due to technological breakthroughs. WSNs generate high traffic due to the growing number of sensor nodes. Congestion is one of several problems caused by the huge amount of data in WSNs. When wireless network resources are limited and IoT devices require more and more resources, congestion occurs in extremely dense WSN-based IoT networks. Reduced throughput, reduced network capacity, and reduced energy efficiency within WSNs are all effects of congestion. These consequences eventually lead to network outages due to underutilized network resources, increased network operating costs, and significantly degraded quality of service (QoS). Therefore, it is critical to deal with congestion in WSN-based IoT networks. Researchers have developed a number of approaches to address this problem, with new solutions based on artificial intelligence (AI) standing out. This research examines how new AI-based algorithms contribute to congestion mitigation in WSN-based IoT networks and the various congestion mitigation strategies that have helped reduce congestion. This study also highlights the limitations of AI-based solutions, including where and why they are used in WSNs, and a comparative study of the current literature that makes this study novel. The study concludes with a discussion of its significance and potential future study topics. The topic of congestion reduction in ultra-dense WSN-based IoT networks, as well as the current state of the art and emerging future solutions, demonstrates their significant expertise in reducing WSN congestion. These solutions contribute to network optimization, throughput enhancement, quality of service improvement, network capacity expansion, and overall WSN efficiency improvement.

Multilevel Adaptive Near-Lossless Compression in Edge Collaborative Wireless Sensor Networks for Mechanical Vibration Monitoring

To address the difficulties of severe lack of storage and computational resources and high delay of transmitting massive vibration data in wireless sensor networks (WSN) for mechanical vibration monitoring (MVM), this paper proposes a novel multilevel adaptive near-lossless compression in edge collaborative WSN (MANLC-ECWSN) for MVM, which could effectively solve the above problems. On the one hand, the sparse pattern of mechanical fault signals is analyzed. A multi-level adaptive near-lossless compression (MANLC) method is proposed to characterize mechanical fault feature information with high accuracy in low storage space, and the proposed method is implemented on the self-developed acquisition node (AN), which effectively improves the storage space and transmission efficiency. On the other hand, the edge computing (EC) technique is integrated into WSN, and data reconstruction and high-precision feature detection are efficiently implemented on the self-developed edge computing node (ECN), which effectively reduces the storage and computing pressure of the data center server. The comprehensive experiments demonstrate that high precision data reconstruction and feature detection could be achieved in the proposed approach from measurements that occupy a little storage space of the AN. The computational power and transmission efficiency of WSN are significantly improved, which provides a potential solution for practical engineering applications.

Secrecy of WSN Data Over Nakagami m Fading Channels with Selection Combining Diversity

We consider the security of wireless sensor network (WSN) data over Nakagami – m fading channels at the physical layer. A WSN in which the fusion center performs selection diversity has been considered for better quality reception. The links between the WSN node and fusion center are assumed to follow Nakagami-m fading distribution. Closed-form expressions for secrecy outage probability (SOP) are derived, and it is established that SOP analysis also leads to the analysis of the existence of secrecy as a special case of SOP. The analytical expressions have been validated through results from simulations. The analysis is valid for all positive real values of the fading parameter, m. The limits on the signal-to-noise ratio can be obtained to secure the transmitted data against eavesdropping with the required SOP and secrecy rate using the analysis presented in this paper.

Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network

Wireless sensor network (WSN) is a multi-hop and self-organizing wireless network consists of fixed or moving sensors, this is one of the key components of the cyber physical system. It jointly senses, gathers, analyses, and transfer the data of detected objects in the network's service area before sending this data to the network's owner. The attacks like, Black hole, Gray hole, Flooding, scheduling are the usual WSN attacks that could quickly harm the system. A significant level of redundancy, network data's higher correlation, intrusion detection schemes for wireless sensor networks also have the drawbacks of poor identification rate, high computation overhead, and higher false alarm rate. MethodsInitially, the data's are taken from WSN-DS. In pre-processing, it confiscates the data redundancy and missing value restore sunder Color Wiener filtering (CWF). In feature selection, the optimal features are selected using tasmanian devil optimization (TDO) algorithm. Based on the optimum features, the intruders in WSN data are categorized into normal and anomalous data utilizing SAPVAGAN. Hence, honey badger algorithm (HBA) is proposed to optimize the SAPVAGAN, which detects the WSN intrusion accurately. ResultsThe proposed technique is performed in Python utilizing the WSN-DS dataset. Here, the performance measures, like recall, precision, f-measure, specificity, accuracy, RoC, computation time is evaluated. The proposed method provides 23.56%, 12.64%, and 15.63% higher accuracy, 23.14%, 16.78% and 20.04% lower computational time analyzed to the existing models, such as Intrusion Detection System in wireless sensor network using light GBM method (ECS-WSN-SLGBM), Intrusion Detection Scheme in wireless sensor network utilizing recurrent neural network (ECS-WSN-RNN) and Intrusion Detection Scheme for Wireless Sensor Networks utilizing whale optimized gate recurrent unit (ECS-WSN-WOGRU) respectively. ConclusionIt combines advanced techniques such as self-attention, provisional learning, and generative adversarial networks. By leveraging self-attention, the model captures important features and relationships in the WSN data. The provisional allows the model to adapt to changing network dynamics. The component generates realistic sensor data and accurately identifies malicious inputs. Overall, this innovative approach improves security and adaptability in WSNs.

A secure and effective data aggregation in WSN for improved security and data privacy

The increasing prevalence of internet usage and mobile devices has underscored the critical importance of safeguarding personal data. This is especially important in Wireless Sensor Networks (WSNs), where information typically requires in-network computing and collaborative processing. These computationally demanding approaches are not suitable for resource-constrained WSN nodes. Aggregating data effectively while protecting user data is a major challenge in wireless sensor networks. Many privacies of homomorphism encryption-based WSN data aggregation methods have been created and investigated recently. Since cluster leaders (aggregators) may rapidly combine cypher texts without decryption, communication overhead is reduced, making these data aggregation methods more secure than traditional ones. However, the base station only receives aggregated output, causing issues. Initial limits apply to aggregating functions. If the aggregated output is the sum of sensing data, the base station cannot acquire the maximum value. Second, attaching message digests or signatures to sensory samples does not allow the base station to validate data authenticity. In dangerous places, WSNs must be energy-efficient and private. In this research, we present a data aggregation method known as Energy-Efficient and Privacy-Preserving (E2P2). E2P2 data aggregation utilizes less energy and yields more accurate results. Private data aggregation with increased accuracy and hybrid encryption is presented in this research. The goal is to reduce data transmission and energy use collisions and offset collision-induced loss. Extensive simulations compare E2P2 to earlier approaches. Experimental results show that E2P2 outperforms other algorithms. Good exactness, complexity, and safety are demonstrated by theoretical and simulation results.

Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm.

Sensor nodes are widely distributed in the Internet of Things and communicate with each other to form a wireless sensor network (WSN), which plays a vital role in people's productivity and life. However, the energy of WSN nodes is limited, so this paper proposes a two-layer WSN system based on edge computing to solve the problems of high energy consumption and short life cycle of WSN data transmission and establishes wireless energy consumption and distance optimization models for sensor networks. Specifically, we propose the optimization objective of balancing load and distance factors. We adopt an improved sparrow search algorithm to evenly distribute sensor nodes in the system to reduce resource consumption, consumption, and network life. Through the simulation experiment, our method is illustrated, effectively reducing the network's energy consumption by 26.8% and prolonging the network's life cycle.

Improved wireless sensor network data collection using discrete differential evolution and ant colony optimization

The main objective of this study is to optimize the performance of a wireless sensor network (WSN) in terms of energy consumption and data routing delay using evolutionary metaheuristics. A WSN mobile sink-based routing method in which the sink moves to certain selected rendezvous nodes for data collection is used to address the hot-spot problem. However, there are two challenges, namely, clustering and mobile sink shortest trajectory traversal, which significantly affect the network energy consumption, lifetime, and delay. To achieve the goal of this study, first, a formal model is presented to solve the optimization problem of determining the optimal number of clusters and corresponding cluster heads, which are taken as rendezvous nodes accessed by the mobile sink considering the network energy consumption, intracluster communication, and transmission delay. Second, a discrete differential evolution algorithm is proposed to solve the formulated optimization problem. Third, an ant colony optimization-based algorithm is proposed to construct the shortest path for the mobile sink to traverse the selected rendezvous nodes. The experimental evaluations show that the proposed strategy significantly improves cluster head selection, balances the cluster members, increases the network lifetime by 54%, decreases the transmission delay by 63%, and reduces energy consumption by 47% compared to several routing strategies in the literature.

Edge Collaborative Compressed Sensing in Wireless Sensor Networks for Mechanical Vibration Monitoring

This paper proposes a novel edge collaborative compressed sensing for mechanical vibration monitoring (MVM) to address the severe shortage of storage and computational resources and long delay in transmitting massive vibration data in wireless sensor networks (WSN) for MVM. It first combines compressed sensing (CS) and edge computing (EC) into a WSN to effectively solve the above problems. Based on the self-developed acquisition node (AN) and edge computing node (ECN), the proposed approach is implemented in the AN and ECN respectively. This enhances the acquisition efficiency and computing capacity of the WSN. Meanwhile, the sparse pattern of a mechanical fault signal is analyzed. In addition, the adaptive parameter adjustment mechanism (APA) for the alternating direction of multiplier method based on Anderson acceleration is proposed to generate a convex optimization algorithm with fast convergence to realize data reconstruction in the ECN. The results of comprehensive experiments demonstrate that the proposed method can reduce the transmission data by 70%, while maintaining high-precision bearing fault feature detection, compared with the Shannon sampling theory. Furthermore, the acquisition and transmission efficiencies are improved significantly. This provides a potential solution for practical engineering applications.

Data monitoring for indirect metering terminal of membrane gas measure device based on sensor network

In order to improve the data monitoring and information management capabilities of membrane gas measure device indirect metering terminals, a sensor network based data monitoring method for membrane gas measure device indirect metering terminals was proposed. The distributed and centralized sensor network node deployment method is used to achieve data collection for membrane gas measure device indirect metering terminals, and the collected membrane gas measure device indirect metering terminal data is segmented, fused, and reorganized. The distributed energy storage and constant volume two-level optimal control method is used to analyze the operation status of gas measure device indirect metering terminal data. The ZigBee networking protocol is used to build a network structure model for gas measure device indirect metering terminals, an integrated high-precision temperature and pressure sensor arranged parallel to the inner wall of the valve body is used to detect the temperature and pressure at the outlet of the membrane gas measure device. The temperature information is stored in DSP, and the user's expenses, system operation and maintenance, and peak shaving and valley filling benefits are taken as the parameters of the energy storage and constant volume outer layer model. The output stability of the indirect metering terminal data of the membrane gas measure device is analyzed in the high switching frequency mode. Combined with adaptive parameter adjustment, the indirect metering terminal control and data dynamic collection of the membrane gas measure device are realized. Realize dynamic monitoring of metering terminal data in wireless sensor network networking control mode. The test results show that using this method for indirect metering terminal data monitoring of gas measure devices has good output stability, strong dynamic fusion of sensing information, reduced metering errors, and improved metering accuracy of gas measure devices.

Research on data fusion of power wireless sensor networks based on Kalman filter

In order to overcome the problems existing in traditional methods such as large mean error and long time of network data fusion, a data fusion of power wireless sensor networks based on Kalman filter is proposed. Firstly, the composition of power wireless sensor is analyzed, and the data of power wireless sensor network is preprocessed. Then, the data fusion process of Kalman filter is designed, and the schematic diagram of the data fusion process is given. Finally, l-M method is used to modify the network data fusion prediction covariance matrix to realize the power wireless sensor network data fusion. Experimental results show that when the amount of data is 600 GB, the data fusion time of the proposed method is 1.89 s. When the number of Kalman recursion is 120, the mean square error of data fusion of the proposed method is 0.04, and the practical application effect is good.

On the design of KLT-based maximally incoherent deterministic sensing matrices for compressive sensing applied to wireless sensor networks data

On the design of KLT-based maximally incoherent deterministic sensing matrices for compressive sensing applied to wireless sensor networks data

A new method based on swarm intelligence with encrypted data in wireless sensor networks

Wireless sensor networks (WSNs) technology is one of the most essential Internet of things technologies. It is utilized efficiently in a variety of real-world applications, including healthcare, environmental monitoring, tracking, etc. WSNs are composed of sensor nodes with restricted resources. However, the communication between WSN components is not secure. Therefore, it is necessary to build efficient and lightweight cryptographic algorithms to secure shared data. Our paper comprises proposes a secure protocol called grasshopper optimization algorithm routing protocol (GOARP) with a lightweight encryption method in each sensor called rivest cipher 5 (RC5) to enhance network efficiency and simulation in terms of power consumption, required memory space, and computational time. Subsequently, the network lifetime result achieved in the proposed method is about (70%) more than in GOA elliptic curve cryptographic and Diffie Hellman (GOA-ECCDH).

A Multi Model Approach: A Data Engineering Driven Pipeline Model for Detecting Anomaly in Sensor Data using Stacked LSTM

Abstract: This study uses a self-supervised learning technique based on auto encoders to find anomalous nodes. Only temporal variables have been taken into account and researched so far for use in identifying anomalies in wireless sensor networks (WSNs). This method fully utilises the geographic and temporal information of the WSN for anomaly identification by incorporating the extraction of geographic location features, intermodal WSN correlation features, and temporal WSN data flow characteristics into the design of the autoencoder. First, by focusing on a single mode from a local spatial perspective, a fully connected network is used to temporal nodes. Second, the spatial and temporal characteristics of the data flows of the nodes and their neighbours are retrieved by concentrating on a specific mode and seeing the WSN topology from a global spatial perspective for anomaly identification. The adaptive fusion method's weighted summation step is then used to extract the relevant features from the various models. An LSTM is used in this study to solve the problem of long-term dependence in the temporal dimension. The decoder's reconstructed output and the hidden layer representation of the are used to calculate the anomaly probability of the current system utilising a fully linked network.

Mechanical equipment fault diagnosis based on wireless sensor network data fusion technology

Abstract To save network energy consumption and prolong network life cycle in complex mechanical fault diagnosis, a research method of data fusion routing protocol algorithm based on wireless sensor network (WSN) is proposed. The specific content of the method is as follows: First, the low-energy adaptive clustering hierarchy algorithm is analyzed and discussed. On this basis, the prim route fusion algorithm is proposed to realize the effective utilization of energy and prolong the life of the network. Then, the WSN is abstracted as an undirected graph. From the perspective of saving the energy of the whole network, several current algorithms for building fusion trees are compared. The experimental results show that the prim algorithm consumes energy only after 700 rounds of clustering, while the leach clustering algorithm consumes energy only after 500 rounds. This shows that applying the prim algorithm can reduce the energy consumption of the whole network and prolong the life cycle of the network. However, the algorithm is carried out on the premise of uniform distribution of nodes, and there is a certain gap with the specific application of WSN in mechanical fault diagnosis. In the comparison of node energy consumption, it is found that compared with using the shortest path tree, using the central point of graph algorithm can greatly save the energy consumption of the node and has better performance. Practice has proved that this method can effectively remove redundant data information and solve the problem of unreliable data collected by a single sensor node. It is more suitable for the specific application of WSN in mechanical fault diagnosis.

A novel chain‐based clustering for green communication in wireless sensor network

SummaryThe rapid growth of the Internet of Things (IoT) creates a high requirement for data collected through wireless sensor networks (WSNs), resulting in a lot of emphasis on WSN data collecting in recent decades. However, the resource‐constrained nature of sensor devices exerts heavy constraints for acquiring the optimal network performance. To resolve this concern, this paper proposes the Chain‐based Energy‐Efficient Clustering (CBEEC) Routing Protocol that considers nodes of two heterogeneous levels of energy (normal level and advanced level). The number of advanced nodes in CBEEC is analytically determined and hence are analytically allocated. However, normal nodes are stochastically deployed in the vicinity of advanced nodes. The data transmission is performed among the advanced nodes in the form of chain and from there it is forwarded to base station (BS). Consequently, it preserves immense amount of energy for the network. The performance of CBEEC is empirically investigated with benchmarks of different performance metrics and simulation outcomes showing that the CBEEC outperforms state‐of‐the‐art routing protocols.

Emerging Routing Method Using Path Arbitrator in Web Sensor Networks

Sophisticated Routing has a big impact on wireless sensor network performance and data delivery. Because nodes join and leave the network on a whim, routing in WSN is not as simple a task as it is throughout sensor networks that are wireless. The fact that the most of WSN devices are resource constrained is another restriction on how routing is implemented in WSN. The WSN uses a variety of routing protocols. However, the primary goal of this research is to determine the best route from the source to the destination using wireless sensor networks and machine learning techniques Which is Particle Swarm Optimization. In this study, an innovative and intelligent machine dubbed the Path Arbitrator or selector, which will store all sensor data and use machine learning methods, is used to develop a new routing mechanism.

Confidentiality Enhancement Using Spread Spectrum Modulation Technique for Aggregated Data in Wireless Sensor Networks

Wireless Sensor Networks (WSN) play a crucial role in transmitting bulk data remotely on Internet of Things (IoT). The data transmitted by the tiny sensor nodes of the network are basically the physical parameters of the environment like temperature, vibration, pressure etc. There exists a huge co-relation in the data sent by the nodes. This bulk data needs to be aggregated in order to conserve the energy of the network and thereby enhance the network lifetime. Although the aggregation might lead to loss of actual data, aggregating helps in reducing energy requirement during transmission which is of primary importance and hence is considered as a suitable method in WSN. Adding confidentiality to aggregated data helps in sending the data more securely. Spread spectrum modulation is a widely used technique to provide confidentiality in communication systems. In this research work, we implement a data aggregation technique and apply the spread spectrum modulation technique to provide confidentiality to the aggregated data that needs to be transmitted from the cluster head node to the gateway. Here, data aggregation process consists of averaging the number of data that are sensed by a sensor node and transmitting only its average value to the gateway. This reduces the amount of data transmission and helps in conserving energy. Further, the spread spectrum technique implements Binary Phase Shift Keying (BPSK) method with Frequency Hopping Spread Spectrum (FHSS) carried out using six different frequencies on MATLAB 2020a. The simulation results evaluate the performance of the system. The graphs are plotted for modulated and demodulated signals, spread and de-spread sequences, Bit Error Rate (BER) of BPSK/ FHSS over Rayleigh flat fading channel, Power Spectral Density (PSD) and Fast Fourier Transform (FFT) of frequency hopped signal. The results show that BER value decreases with increase in Signal to Noise Ratio (SNR). The maximum power consumption in the network is 7.518 mW at 5MHz frequency, for adding confidentiality to the aggregated data to be transmitted. Thus, the proposed work promises efficient energy consumption, longer network lifetimes with added confidentiality.

Multiobjective 3-D UAV Movement Planning in Wireless Sensor Networks Using Bioinspired Swarm Intelligence

The use of unmanned aerial vehicles (UAVs) is a promising solution to efficiently acquire data in large-scale wireless sensor networks (WSNs). In a wide-area WSN environment, UAV path planning is one of the challenging issues in solving the optimization problem to achieve complex and multiple objectives under various constraints related to UAV operation. In this paper, we propose a novel asynchronous UAV path planning mechanism for multi-objective UAV operation. In a three-dimensional sensor field, a grid-based sensor field and information gathering model is introduced. We define a UAV coverage area where line-of-sight communication is possible between a UAV and the sensors and propose a method to quickly find the grid-cells within the coverage area. We define a multi-purpose fitness function that maximizes the value of the acquired sensing information and at the same time minimizes the time and energy required for UAV operation. The value of the sensing information reflects the sensor density for each sensor type within UAV coverage, as well as changes in the sensing information values over time. In the proposed method, time and energy objective functions are learned by considering location-dependent communication link quality, sensor density, and the next UAV locations. The optimum UAV position and the movement schedule of each UAV are asynchronously derived using the proposed particle swarm optimization (PSO) algorithm with several UAV operational constraints. The experiment results demonstrate that the proposed method can maximize the utility of the objective function and achieve fast convergence in finding the optimal solution compared with other methods.

In-Network Processing or Feature Compressive Sensing? Case Study of Structural Health Monitoring With Wireless Sensor Networks

In many domain-specific monitoring applications of wireless sensor networks (WSNs), such as structural health monitoring (SHM), volcano tomography, and machine diagnosis, all the raw data in WSNs are required to be gathered to the sink where a specialized centralized algorithm is then executed to extract some global features or model parameters. To reduce the large-scale raw data transmission while guaranteeing the global feature quality, there are two kinds of solutions: one is in-network processing, which generally needs to distribute the centralized computation of feature extraction into networks. Another solution is compressive sensing (CS) followed with the feature extraction (called feature CS in this article). An interesting question is: for in-network processing and feature CS, which kind of solutions is more cost efficient to accomplish the task of feature extraction? This question is seldom studied. To answer it, we take the case of SHM with WSNs along with the classic feature extraction algorithm, i.e., the Eigen-system realization algorithm (ERA), and appropriately design two novel routes for in-network processing and feature CS, respectively. Both theoretical analysis of the two solutions’transmission cost and numerous simulations have been conducted. Based on the comparison results, we summarize some guidelines on the solution choice for different kinds of WSNs for SHM. In addition, we find that, instead of guaranteeing the quality of raw data reconstructed, CS with guaranteeing the quality of feature extracted is usually more meaningful and cost efficient.

Multilayer Joint Optimization of Packet Size and Adaptive Transmission Scheduling of Wireless Sensor Networks for Mechanical Vibration Monitoring

Limitations pertaining to the storage capacity and bandwidth of wireless sensor networks (WSNs) for mechanical vibration monitoring (MVM) pose difficulties in the realization of reliable and energy-efficient communication of big data. To prolong the lifetime of battery-limited WSNs, this paper proposes a multi-layer joint optimization of packet size and adaptive transmission scheduling (ATS) to jointly optimize the energy consumption of acquisition nodes from the storage and transmission layers. First, owing to the frequent storage and transmission of massive mechanical vibration data in WSNs, the packet size significantly affects the energy consumption during storage and transmission. The proposed packet size optimization based on the lowest energy consumption (PSO-LEC) model optimizes the energy consumption of acquisition nodes from three aspects: writing, reading, and transmission of data. Second, when transmitting massive vibration data frequently, the direct beacon frame conflict and indirect beacon frame conflict markedly induce data conflict and delay problems. Therefore, adaptive transmission scheduling based on the time division multiple access (TDMA/ATS) method is proposed to optimize the errors in data collision and transmission delay. Thus, the packet size and ATS are considered jointly to improve the network lifetime for MVM. Comprehensive experimental results indicate that the transmission reliability and energy-efficiency of the proposed scheme are significantly improved, and the network life is effectively prolonged for MVM.