Transmission In Wireless Sensor Networks Research Articles

Energy Efficient Approach with ANN based Intrusion Detection System for Securing Network and Route for Transmission in Wireless Sensor Network

The research focuses on hierarchical clustering-based intrusion detection using artificial neural networks (ANNs) for secure data transmission in wireless sensor networks (WSNs). WSNs consist of numerous tiny sensor nodes deployed to monitor environmental phenomena. These nodes face significant challenges, including restricted energy, memory space, communication range, and limited capacity for managing energy, storing, transmitting, and processing data. To address these limitations, a machine learning-based approach is proposed to detect intrusions and efficiently utilize energy by properly selecting cluster heads using a secure clustering protocol. The proposed method was implemented and tested using MATLAB software, employing the NSLKDD and UNSW-NB15 datasets for intrusion detection. The results demonstrated promising outcomes in detecting intruders and enhancing network efficiency, achieving a 92% packet delivery ratio (PDR) and 1.82 Mbps throughput. The study concludes that while WSNs are gaining popularity due to their simplicity, flexibility, and scalability, innovative solutions are necessary for efficient energy management and security. Future research should focus on advanced machine learning models, energy harvesting techniques, scalable protocols, real-time data processing, and integration with IoT platforms for broader applications and enhanced functionality.

Aquila Optimized Fuzzy Deep Belief Network for Secure Data Transmission in WSN

A wireless sensor network (WSN) is made up of several independent sensor nodes that are able to interpret, analyze, and work with data. It is generally recognized that security and limited energy are the two challenging tasks with WSNs. To address these challenges, a novel Aquila-optimized fuzzy deep belief network (AO-FDBN) model has been proposed in this paper. The suggested AO-FDBN framework consists of three stages. Initially, the Aggregator has been selected by using the Aquila optimization algorithm. Secondly, the data from the aggregator are encrypted by using the blowfish algorithm. Finally, the optimal route has been selected by using the fuzzy-deep belief network (DBN). Packet delivery ratio (PDR), transport delay, energy usage, and network lifetime are evaluated between the suggested framework and current methods. Experimental results specify that the suggested AO-FDBN approach achieves higher performance of 22.617%, 14.22%, and 15.64% than TEEFCA, HESC, and SEPC methods. This system is more effective and secure for real-time applications.

Correction to: Certificate-less Aggregate Signature Authentication Scheme (CLASAS) for secure and efficient data transmission in Wireless Sensor Networks (WSNs)

Correction to: Certificate-less Aggregate Signature Authentication Scheme (CLASAS) for secure and efficient data transmission in Wireless Sensor Networks (WSNs)

Enriched energy optimized LEACH protocol for efficient data transmission in wireless sensor network

Enriched energy optimized LEACH protocol for efficient data transmission in wireless sensor network

Deep Learning-CT based spectrum sensing for cognitive radio for proficient data transmission in Wireless Sensor Networks

Spectrum sensing is a vital element of cognitive radio networks. It's the basis for unlicensed users to access underutilized bands without disturbing licensed or primary users (PUs). Deep learning has given a better way to analyze signals compared to energy detection and other conventional methods that were present earlier. This research introduces a CNN-TN-based spectrum approach that integrates both CNNs and transformer networks (TNs) to maximize efficient spectrum utilization. CNNs are good at finding features in signals, which helps them pick out important features from the IQ-based spectrum data. Transformer networks, on the other hand, are able to take into account long-range dependencies over time, which can help improve and fine-tune these features even more. This method achieves low sensing errors and a better probability of detection by capturing both local and global patterns in the spectrum data. Performance measures like Cohen's Kappa coefficient and F1 score show that this method's performance is better than earlier methods when it comes to performance. These findings also suggest that real-world wireless communications systems could potentially use the proposed methodology, thereby improving overall usage and indicating its potential for practical applications in the field.

Data Transmission in Wireless Sensor Networks Based on Ant Colony Optimization Technique

A wireless sensor network (WSN) is a wireless network composed of sensor nodes, widely used in military, industrial, and agricultural fields. In these practical application scenarios, due to the limited routing search ability of wireless sensor nodes and the small coverage range of a WSN, it faces the problem of a relatively short network lifecycle. Therefore, increasing a WSN’s coverage range and reducing node energy consumption are of high value. This article proposes a method of using the ant colony algorithm (ACOD) to calculate the optimal routing of a WSN, the K-means algorithm for clustering, and the whale algorithm (WOA) and a backpropagation (BP) neural network to increase the coverage range of a WSN. After multiple experiments, the total energy consumption of WSN nodes has been effectively reduced, the coverage range of the WSN has been increased, and the lifecycle of the WSN has been extended.

Certificate-less Aggregate Signature Authentication Scheme (CLASAS) for secure and efficient data transmission in Wireless Sensor Networks (WSNs)

Certificate-less Aggregate Signature Authentication Scheme (CLASAS) for secure and efficient data transmission in Wireless Sensor Networks (WSNs)

Enhanced Secured and Real-Time Data Transmissions in Wireless Sensor Networks using SFRT Routing Protocol

Enhanced Secured and Real-Time Data Transmissions in Wireless Sensor Networks using SFRT Routing Protocol

Spectrum sensing using deep learning for proficient data transmission in wireless sensor networks for wireless communication

Cognitive radio has been recommended for enhancing wireless spectrum usage, and an important part of cognitive radio is spectrum sensing. Conventional spectrum sensing techniques rely on energy detection and feature extraction from a received signal at a specific position. The advancement of artificial intelligence and deep learning has provided a chance to increase spectrum sensing efficiency. The proposed investigation presented a hybrid model comprising CNN, BILSTM, and Transformer Networks (TN) for spectrum sensing. With the aid of Transformer networks, the proposed model improves sensing accuracy even for lower SNR signals, particularly at −20 dB. The results of the proposed technique show that CNN-BILSTM-TN enhances spectrum sensing in comparison to earlier models studied in this domain. The proposed approach outperforms prior methods in terms of F1 Score and has an improved probability of detection at −20 dB and a lower likelihood of missed detection. Analysis of ten modulation techniques' performance criteria, such as the Jaccard index, Cohen Kappa coefficient, and Matthew's correlation coefficient, demonstrates that the suggested method's spectrum sensing is better even at lower SNRs.

DynAuthRoute: Dynamic Security for Wireless Sensor Networks

Objectives: The research aims to design an architecture for secure transmission of data in wireless sensor networks. Methods: The method involves three main pillars: authentication, data encryption, and dynamic routing. Extensive simulations have been conducted to evaluate the suggested method in terms of energy consumption, memory footprint, packet delivery ratio, end-to-end latency, execution time, encryption time, and decryption time. Findings: For authentication, a dynamic key is used to power an improved salt password hashing method. Data encryption is performed using format-preserving encryption (FPE) with the appended salt key. Dynamic routing is implemented using a cluster-based routing technique to enhance network efficiency in terms of power consumption and security. The execution time for MD5 ranges from 15 to 22 milliseconds, while for SHA-1 it ranges from 16 to 23 milliseconds and for the proposed salt key generation it is 1 to 5 milliseconds. Similarly, in terms of energy consumption, memory footprint, packet delivery ratio, end-to-end latency, execution time, encryption time, and decryption time the proposed method shows promising results in ensuring the integrity and security of transmitted encrypted data. Novelty: The presents a novel architecture with enhanced cluster head-based selection algorithm that combines dynamic key-based authentication and secure data routing to establish a safe environment for data transmission in wireless sensor networks. This research works offers a method for encrypting text with a dynamic salt key that is safe, energy-efficient, and lightweight. Keywords: Wireless Sensor Network, Dynamic Key, Authentication, Hash function, Salt algorithm, Dynamic routing, Node clustering, Format-preserving encryption

Evaluation on Data Transmission in Wireless Sensor Networks Based on Computer Technology

Traditional network data transmission (DT) has certain limitations in terms of processing, storage, and communication capabilities. Moreover, DT is easily affected by the network environment, which can reduce the real-time performance of DT. In severe cases, network transmission failures, DT interruptions, and other issues may even occur. As a new network, wireless sensor networks (WSN) have been widely used in military, industrial, agricultural, medical and other fields. In this paper, WSN was regarded as an important research method, and in-depth research was carried out around the real-time and energy consumption (for the convenience of the following text, the abbreviation for energy consumption is EC) of WSN DT. This paper focused on the issues of real-time performance, EC, and real-time transmission. It balanced EC and improved real-time performance through the low energy adaptive clustering hierarchy (LEACH) protocol and the power-efficient gathering in sensor information systems (PEGASIS) protocol. The experimental results showed that the real-time rates of LEACH under single reaction nodes were 45.5% and 48.6%, respectively. The lowest and highest real-time rates of PEGASIS were 86.2% and 89.5%, respectively. The real-time rate of PEGASIS was much higher than that of LEACH, proving that the proposed PEGASIS had high real-time performance and reduced DT delay.

Efficient key distribution for secure and energy-optimized communication in wireless sensor network using bioinspired algorithms

Wireless Sensor Networks (WSNs) play a major part in numerous applications such as smart agriculture, healthcare, and environmental monitoring. Safeguarding protected communication in this network is dominant. Securing data transmission in WSNs needs a strong key distribution device to defend against malicious attacks as well as illegal access. Traditional techniques like pre-shared or centralized key management are often unreasonable owing to resource limitations, particularly in large-scale sensor systems. To overcome this challenge, a lightweight key distribution technique is employed for safeguarding the security and privacy of data transmission streamlining processes decreasing computational overhead as well as energy consumption. By optimizing and simplifying key distribution devices, we propose to improve the complete efficacy and trustworthiness of WSNs that aid safe communication while preserving valuable energy resources. Therefore, this article designs an Efficient Key Distribution for Secure and Energy-Optimized Communication using Bioinspired Algorithms (EKD-SOCBA) for WSN. The purpose of the EKD-SOCBA technique is to accomplish security and energy efficiency in WSNs. Initially, the EKD-SOCBA technique applies a golden jackal optimization (GJO) based clustering approach to cluster the nodes and select cluster heads (CHs). Also, a lightweight Dynamic Step-wise Tiny Encryption Algorithm (DS-TEA) is applied to secure data transmission in the network. Finally, a lightweight key management phase is employed to protect the encryption key and decrease energy utilization and overhead costs. To exhibit the enhanced act of the EKD-SOCBA model, a comprehensive set of imitations was involved. Extensive results stated enhanced presentation of EKD-SOCBA methodology over other models on WSN.

VAWGAN-NPOA: Energy-Aware Routing for Innocuous Data Transmission in WSN

The protection and prediction of harmful activity in the wireless sensor network (WSN) are vital research problems. To solve these problems, an Energy-Aware Routing using a Variational auto-encoder Wasserstein generative adversarial network with a Nomadic People Optimizer (VAWGAN-NPOA-EAR-WSN) is proposed in this paper for secure data transmission in WSN. The proposed Variational auto-encoder Wasserstein generative adversarial network (VAWGAN) selects the Cluster Head (CH) based on multiple objective fitness functions such as delay, distance, energy, cluster density, and traffic rate. After that, energy-aware trust path selection is selected based on parameters such as trust, connectivity, and degree of amenity using the proposed Nomadic People Optimization Algorithm (NPOA). The data are transmitted from the cluster head to the base station and vice versa via a perfect trust path. The proposed VAWGAN-NPOA-EAR-WSN method is done in MATLAB and its efficacy is assessed with performance metrics, such as energy consumption, network lifetime, throughput, etc. compared with existing such as DNN-WSN, EESNN-WSN, and ANN-WSN. Finally, the proposed method provides a lower energy consumption of 2 mJ compared with existing methods.

A public static agreement key based cryptography for secure data transmission in WSN based smart environment application

Wireless Sensor Networks (WSN) is an emerging networking technology that allows for the low-cost, unattended monitoring of a variety of environments, thus WSN technologies have become increasingly important. Simultaneously, lack of security and poor energy efficiency is considered the major concerns in many routing protocols in various existing WSN protocols. Security and energy efficiency concerns are mainly due to the emergence of various kinds of malicious attacks because of advancements in networking technology. To mitigate these issues secured transmission is essential in any routing protocol. In order to provide safe data transfer in WSN-based smart environment applications, the TABEESR protocol (Trust Analysis Based Energy Efficient and Safe Routing) was developed. The proposed routing protocol encompasses four phases such as network creation and node deployment, grading for trust evaluation, route identification process and data transmission. The trust evaluation is performed with the aid of sensor packet features to categorize the positive nodes and negative nodes. The path identification process includes two processes such as cluster head selection through red fox optimization and relay node selection using a fuzzy interference system for energy efficient and secure path selection. A public static agreement key based cryptography technique is introduced for secure data transmission. The proposed TABEESR is tested with MATLAB software to validate its performance. SMEER, SHER, SRPA and AFSA are the prior routing protocols in WSN that are considered to compare the performance of the proposed routing protocol. The proposed TABEESR algorithm attained 95(%) of Average Residual Energy, 91 (%) of packet delivery ratio, 1350 (sec) network lifetime, 0.131 (Mbps) of throughput and 7(%) Packet Loss. Moreover, the proposed PSAKC algorithm attained 5.8 (%) information loss, 3.8 (sec) encryption time, 2.3 (sec) decryption time, 6 (sec) execution time, and 6.5 (J) energy consumption which is better than the prior methods. The proposed routing algorithm is applicable for providing security in both server to device and device to device in various smart applications.

Trust Aware Nero Fuzzy Based Agglomerative Hierarchical Clustering with Secure Whale Optimization Routing for Enhancing Energy Efficiency in WSN

Wireless sensor networks (WSNs) comprise a network of dispersed, carefully positioned sensor nodes in their deployment environment to monitor and collect data on natural phenomena. These sensor nodes collaborate to transmit data via multi-hop communication, ultimately reaching a central base station for processing. However, WSNs face significant challenges due to the resource-constrained nature of these devices and the harsh, open environments in which they operate. Addressing energy optimization and ensuring secure communication are primary concerns in the successful operation of WSNs. This paper introduces anovelTrust aware Neuro Fuzzy Clustering head selection (TNFCH) and agglomerative hierarchical clustering approach (AHC) with Secure Whale Optimization (SWO) Algorithm Routing to enhance energy-efficient transmission in WSNs. Our proposed protocol (TNFCH-AHWO) efficiently organizes nodes by utilizing neural network and Fuzzy logic then securely transfers the data into the communication network. We employ a Trust calculation algorithm in our system to ensure Trust and data integrity, facilitating efficient lightweight operations such as key generation, encryption, decryption, and verification. This ensures hop-to-hop authentication among the nodes in WSNs. To assess the performance of our proposed protocol, we conducted simulations using the NS3 simulator. The findings of the simulation show that the suggested protocol greatly enhances various performance metrics, including energy consumption analysis, throughput, network delay, network lifetime, and packet delivery ratio when compared to existing protocols.

Design of Enhance Co-operative Balanced Route Protocol (CBRP) in WSNs

Wireless Sensor Networks (WSNs) are instrumental in various applications, including environmental monitoring, healthcare, and smart infrastructure. However, ensuring efficient and reliable data transmission in WSNs remains a challenge due to the dynamic nature of the network topology and limited resources of sensor nodes. This paper proposes an enhanced Cooperative Balanced Route Protocol (CBRP) for WSNs to improve routing efficiency and energy consumption. The proposed protocol introduces a cooperative approach where sensor nodes collaborate to relay data packets, balancing energy consumption and prolonging network lifetime. Simulation results demonstrate that the enhanced CBRP outperforms existing protocols in terms of network throughput, end-to-end delay, and energy efficiency, making it a promising solution for WSN applications.

Integrated Shared Random Key Agreement Protocol for Wireless Sensor Network

The secured data transmission in Wireless Sensor Network (WSN) relies on effective key generation and secured sharing. The generated key must be random to enhance data confidentiality. The processes associated with the security in WSN must be designed at reduced computing time and communication cost. Our research work aims to design a novel lightweight key-sharing protocol that is needed for ensuring data confidentiality. The protocol must meet the constraints of WSN by being lightweight and consuming less energy. Security breaches in WSNs occur due to insecure keys. This can be overcome by generating shared keys which are generated once using the dynamic features of Sensor Nodes (SNs) when the Cluster Heads (CHs) are selected. In this research work, we have generated the Master Shared Key (MSK) at the transmitter node by forming a Galois Ring (GR) using WSN parameters and derived the Shared Random Key (SRK) using matched positions of exchanged Random Sequences (RSs). It is protected using a Physically Unclonable Function (PUF). The novelty lies in the SRK generation from MSK which is chosen at random from the polynomials generated during the formation of GR. The MSK is securely shared with the receiver node by encrypting using a Preloaded Key (PK). After this exchange, the key for encryption and decryption is derived by the transmitter and the receiver by exchanging RSs. The SRK is then encrypted using a key which is a unique fingerprint of the SN generated using PUF and stored in the SNs and the CHs to prevent node capture attack that occurs in WSN. Our proposed Shared Random Key Agreement Protocol (SRKAP) is comparable to the Localized Encryption and Authentication Protocol (LEAP) and performs better compared to the Elliptic Curve Diffie Hellman (ECDH) algorithm

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

Hybrid Compression Algorithm for Energy Efficient Image Transmission in Wireless Sensor Networks Using SVD-RLE in Voluminous Data Applications

Hybrid Compression Algorithm for Energy Efficient Image Transmission in Wireless Sensor Networks Using SVD-RLE in Voluminous Data Applications

Energy-Efficient One-to-One and Many-to-One Concurrent Transmission for Wireless Sensor Networks

Energy-Efficient One-to-One and Many-to-One Concurrent Transmission for Wireless Sensor Networks