Transmission In Wireless Sensor Networks Research Articles

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.

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

Modeling and Analyzing Urban Sensor Network Connectivity Based on Open Data

The optimization of network topology is crucial to achieve efficient data transmission in wireless sensor networks. Recently it has been proven that emerging open data sources can be used for modeling the structures of heterogeneous urban sensor networks. With this, leveraging real location data of various networked and sensing devices became feasible and essential. This approach enables the construction and analysis of more accurate representations based on frequently updated actual network infrastructure topology data, as opposed to using synthetic models or test environments. The presented modeling method serves as the basis for the designed architecture and implemented research environment. This paper introduces a set of algorithms which transform devices’ location data into graph-based wireless network connectivity models. Each algorithm is thoroughly discussed and evaluated. Moreover, static (momentary) and dynamic (time-spanning) network topologies are constructed in four large Polish cities based on publicly available data. Multidimensional simulation-based analysis is conducted to investigate the characteristics of the modeled structures. Directions for further research are suggested as well.

Intelligent Deep Learning and Softmax Routing for Energy-Efficient Wireless Sensor Networks in Public Space Design

The increasing usage of several nodes to transfer the massive volume of data to the remotes in wireless sensor networks is a challenging task to reduce the loss. The high volumes of data transmission in wireless sensor networks (WSN) can surpass their capacity, resulting in congestion, latency issues, and packet loss. However, computational intelligence (CI) models can aid in managing and creating intelligent networks in WSN. The WSN congestion issues result in information loss and increased energy usage. CI-based models have been used to resolve this issue, reducing the latency. This paper proposes SoftMax Routing with Deep Neural Network (SRDNN) for efficient routing in WSN. This will route the data packets by choosing the high energy and lower load. It consists of two parts, such as the construction of the routing path, which determines the residual energy of the node. It is analyzed using SoftMax routing to decide whether the node is efficient in energy. The route request and reply established various paths between the source and destination. The path with minimum buffer space and maximum bandwidth is chosen in the optimal routing. The simulation results under the metrics such as energy consumption, data loss rate, throughput, and delay show the proposed model performance.

Application of corporate image integrating Zigbee technology in brand APP interface design using IoT technology

In modern social APP interface design, how to effectively improve the corporate image and create the connotation of corporate culture is a significant key problem. With the emergence of APP, a growing number of people use them, increasing communication energy usage and slowing network operation. To improve app compatibility and speed, it is necessary to combine it with the most advanced and dependable technology, such as ZigBee, which is regarded as the best solution for wireless sensor networks. The ZigBee protocol is primarily used to incorporate working and data transmission in wireless sensor networks that are based on ZigBee technology. As a result, incorporating ZigBee technology into APP interface design in the Internet of Things (IoT) domain can significantly improve brand APP interface design’s network operation efficiency. This paper presents a novel approach to enhance the performance and corporate image of brand mobile applications (APPs) by integrating ZigBee technology. The primary objective is to improve the operating efficiency and user experience of the brand APPs. The study involves a comparison between 10 brand APPs that have not integrated ZigBee technology and 10 brand APPs that have adopted ZigBee technology. The experimental results indicate that the operating efficiency of the brand APPs incorporating ZigBee technology is 97%, while the efficiency of the brand APPs without ZigBee technology is 85%, resulting in a notable difference of 12%. To assess the effectiveness of ZigBee technology integration, the study conducted experiments with 100 users, randomly assigned to interact with both types of brand APPs. The user feedback and observations revealed that brand APPs integrated with ZigBee technology exhibit significantly higher operating efficiency, contributing to a 12% improvement over their counterparts lacking ZigBee integration. Moreover, 90 out of 100 users reported a preference for the brand APPs integrated with ZigBee technology due to their superior user experience. The integration of ZigBee technology in brand APPs not only enhances the user experience but also contributes to the improvement of the company’s corporate image. Adopting ZigBee technology in brand APPs is a valuable strategy that can facilitate the long-term development and success of the company.

Cluster-Disjoint Multipath Routing Protocol for Real-Time and Reliable Packet Transmission in Wireless Sensor Networks.

Multipath routing protocol, which is one of the typical schemes to improve end-to-end transmission success ratio in Wireless Sensor Networks (WSNs), exploits two or more paths. However, collisions and interference might occur when each path is adjacent. To prevent this problem, they construct node- or link-disjointed paths. Although the paths could prevent the above-mentioned problem, it still has an issue in the area of time delay. To exploit the disjointed paths, pre-path construction is required. In addition, a lot of time is incurred to recover the whole path when the part of the path is damaged. This phenomenon adversely affects the end-to-end transmission success ratio and transmission time. To alleviate this problem, we propose a cluster-disjoint multipath routing protocol. The proposed scheme divides the whole network into grid clusters of a certain size in the network initialization phase. Each sensor could transmit packets through the pre-constructed cluster structure without the path construction phase. Also, even if any node fails, it could be easily replaced by other nodes within the cluster region. The simulation results show that the proposed scheme has an advantage in terms of transmission delay and end-to-end transmission success ratio compared to existing multipath routing protocols.

Enhanced cryptography algorithm and improved butterfly algorithm for secured data transmission in wireless sensor network

Due of their many uses, WSNs (Wireless Sensor Networks) have drawn the greatest interest, but,existing methods do not support reliable data transfer. They face issues in energy consumptions and routing on shortest paths over WSNs. Also, they are incapable to stopping compromised nodes with legitimate identities from launching attacks. This study handled this issue with suggested schema basedon CHs (Cluster Heads) and using IBO (Improved Butterfly Optimization) and DAES (Double key based Advanced Encryption Standard) algorithms. The main phases of this work contain system model, security model, CH node selection and shortest path routing with secured data transmission. Initially,system model is constructed via number of sensor nodes on the given setup. Then, CH nodes areselected using IBO algorithm based on best fitness values. These selections of CHs consider minimum delays and energy consumptions with maximum throughputs for establishing security assurances and energy conservations at sensor nodes along with secure protocol. The security levels for quick datatransfers over multi-path routes in WSNs are improved using DAES. Attack nodes are eliminated for successful transfers. The intermediate layer CHs create routing backbones to gather, combine, and convey data from member nodes. The simulation results demonstrate that the proposed IBO-DAES framework outperforms existing approaches in terms of throughputs, network longevity, data transfer rates, and energy consumptions.

Secrecy Performance Analysis of Cooperative Multihop Transmission for WSNs under Eavesdropping Attacks.

Multihop transmission is one of the important techniques to overcome the transmission coverage of each node in wireless sensor networks (WSNs). However, multihop transmission has a security issue due to the nature of a wireless medium. Additionally, the eavesdropper also attempts to interrupt the legitimate users' transmission. Thus, in this paper, we study the secrecy performance of a multihop transmission under various eavesdropping attacks for WSNs. To improve the secrecy performance, we propose two node selection schemes in each cluster, namely, minimum node selection (MNS) and optimal node selection (ONS) schemes. To exploit the impact of the network parameters on the secrecy performance, we derive the closed-form expression of the secrecy outage probability (SOP) under different eavesdropping attacks. From the numerical results, the ONS scheme shows the most robust secrecy performance compared with the other schemes. However, the ONS scheme requires a lot of channel information to select the node in each cluster and transmit information. On the other side, the MNS scheme can reduce the amount of channel information compared with the ONS scheme, while the MNS scheme still provides secure transmission. In addition, the impact of the network parameters on the secrecy performance is also insightfully discussed in this paper. Moreover, we evaluate the trade-off of the proposed schemes between secrecy performance and computational complexity.