Intelligent Wireless Sensor Network Research Articles

Faulty Nodes Detection for Reliable Data Transmission in Intelligent Wireless Sensor Networks

Faulty Nodes Detection for Reliable Data Transmission in Intelligent Wireless Sensor Networks

Retracted: Research on Interactive Design Algorithm of Folk Museum Exhibit Map Based on Intelligent Wireless Sensor Network

Retracted: Research on Interactive Design Algorithm of Folk Museum Exhibit Map Based on Intelligent Wireless Sensor Network

ИНТЕЛЛЕКТУАЛЬНАЯ БЕСПРОВОДНАЯ СЕНСОРНАЯ СЕТЬ С ФУНКЦИЕЙ ПОТОКОВОГО РАСПОЗНАВАНИЯ МНОГОЧАСТОТНЫХ СИМВОЛОВ

Intelligent wireless sensor networks are an innovative class of technologies that combine wireless communications and sensor information systems to create dynamic and flexible data collection and analysis systems. The article proposes a variant of the method for stream recognition of multi-frequency symbols generated by a group of information sources with the possibility of significantly improving the energy and information indicators of the data transmission channel.

Deployment Optimization of Intelligent Wireless Sensor Network Using Graph Similarity Based on Multi-Granularity Cross Representation and Matching

Deployment Optimization of Intelligent Wireless Sensor Network Using Graph Similarity Based on Multi-Granularity Cross Representation and Matching

Intelligent Traffic Data Transmission and Sharing Based on Optimal Gradient Adaptive Optimization Algorithm

This work aims to improve the transmission and sharing efficiency of intelligent transportation data and promote the further development of intelligent transportation and smart city. In this work, an EEMR (Energy Efficient Multi-hop Routing) is designed for the intelligent transportation wireless sensor network. In the EEMR algorithm, the base station runs the IAP clustering algorithm for network clustering after receiving the information of all surviving nodes. A method called ACRR (Adaptive Cluster-head Round Robin) is proposed for local dynamic election of cluster heads. In addition, the deep learning-based stochastic gradient descent algorithm and its evolution algorithm are sorted out, and its application in practical scenarios is analyzed. Due to the disadvantages of the adaptive algorithm in the current data processing process, the gradient optimization algorithm based on deep learning is adopted and the concept of adaptive friction coefficient is applied to the Adam algorithm to obtain a new adaptive algorithm (TAdam). The simulation experiment reveals that the proportion of network surviving nodes of the EEMR algorithm is still as high as 90% in 1,500 rounds of data collection. This shows that the EEMR algorithm achieves the energy balance of the network nodes as much as possible while minimizing the system energy consumption. In application scenario 1, when the network surviving nodes of the four data collection algorithms compares dropped below 40%, the number of surviving nodes in the EEMR algorithm is still as high as 97.6%. On the PTB (Penn Tree Bank) test data set, the TAdam algorithm shows the fastest convergence speed and the best generalization performance. The TAdam algorithm based on deep learning discussed in this work was of great significance for improving the transmission and sharing efficiency of intelligent transportation data.

Tensor and Confident Information Coverage Based Reliability Evaluation for Large-Scale Intelligent Transportation Wireless Sensor Networks

Wireless sensor network (WSN) plays an important role in information collection and processing in Intelligent Transportation Systems (ITS) recently. With rapid development of ITS, the size and complexity of WSN is rapidly increasing. Thus, how to evaluate the reliability of large-scale WSN has attracted more and more attention. In this paper, reliability is defined as the probability that the WSN is functional. A confident information coverage (CIC) model-based reliability algorithm (CICRA) is proposed to comprehensively consider coverage reliability and connectivity reliability. Especially, to determine the impact of failing nodes on connectivity in large-scale WSN, a grid clustering connectivity algorithm (GCCA) is proposed to reduce the complexity of computing the connectivity between large-scale wireless sensor nodes, which transforms the connectivity problem between nodes into a grid connectivity problem. In addition, a 3-order tensor modeling is proposed to uniformly represent and model node states, node combinations, and coverage patterns. Furthermore, the network reliability combines coverage reliability and connectivity reliability is evaluated by tensor modeling. Simulation results indicate that the proposed CICRA can improve the network reliability and accuracy of reliability evaluation.

A Novel Chaotic Elite Adaptive Genetic Algorithm for Task Allocation of Intelligent Unmanned Wireless Sensor Networks

In recent times, the progress of Intelligent Unmanned Wireless Sensor Networks (IUWSNs) has inspired scientists to develop inventive task allocation algorithms. These efficient techniques serve as robust stochastic optimization methods, aimed at maximizing revenue for the network’s objectives. However, with the increase in sensor numbers, the computation time for addressing the challenge grows exponentially. To tackle the task allocation issue in IUWSNs, this paper introduces a novel approach: the Chaotic Elite Adaptive Genetic Algorithm (CEAGA). The optimization problem is formulated as an NP-complete integer programming challenge. Innovative elite and chaotic operators have been devised to expedite convergence and unveil the overall optimal solution. By merging the strengths of genetic algorithms with these new elite and chaotic operators, the CEAGA optimizes task allocation in IUWSNs. Through simulation experiments, we compare the CEAGA with other methods—Hybrid Genetic Algorithm (HGA), Multi-objective Binary Particle Swarm Optimization (MBPSO), and Improved Simulated Annealing (ISA)—in terms of task allocation performance. The results compellingly demonstrate that the CEAGA outperforms the other approaches in network revenue terms.

To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

AbstractThe growing cognizance of spectrum scarcity has become a more significant concern in wireless radio communications. Due to the exponential growth of data transmission in intelligent wireless sensor networks, energy spectrum detection has become a promising solution for resolving spectrum shortages. Primary user emulation attack (PUEA) has been identified as a significant attack vector in the cognitive radio (CR) domain's physical layer. In comparison, the CR is a promising method to increase spectrum efficiency by allowing unlicensed secondary users (SUs) to access licensed frequency bands without interfering with approved primary users (PUs). The study's primary findings are the methodology for preventing PUEA using authentication tags, which are unique sequences. This research blends SC‐FDMA with CR to protect CR networks from PUEA attacks, a Latin square (LS) matrix tag generation system is proposed to mitigate the PUEA effect. The technology is meant to provide effective authentication and protection against malicious users. In a secured environment, the LS tag technique is utilized to track and estimate the PU. For example, the BER of both techniques is virtually identical between 0 and 4 dB, while the BER performance of the suggested LS tag generation improves with increasing signal‐to‐noise ratio (SNR). As a result, the suggested LS tag generation is less susceptible to PUEA. To diminish the influence of PUEA in CR networks, an efficient enlightening approach for making the future Green Cognitive Radio Wireless networks structure is proposed. The simulation results also demonstrate the resilience of the proposed CR spectrum sensing techniques for energy‐efficient knowledge at varying degrees to reduce the adverse effects of environmental technologies.

Retracted: Optimization of Intelligent Display Mode of Museum Cultural Relics Based on Intelligent Wireless Sensor Network

Retracted: Optimization of Intelligent Display Mode of Museum Cultural Relics Based on Intelligent Wireless Sensor Network

Retracted] Research on Interactive Design Algorithm of Folk Museum Exhibit Map Based on Intelligent Wireless Sensor Network

With the continuous improvement of people’s living standards, people’s pursuit of material life and satisfaction of spirituality are also increasing, and the demand for culture and value orientation are also diversifying. As an important position of many cultural consumption places, museums have seen an unprecedented increase in their cultural radiation and public attention. In such a large social environment, the nature museum industry increasingly recognizes that to make full use of the role of museums and better reflects the characteristics of the industry, it is necessary to introduce visual image design into museums. However, visual imagery design in domestic museums is still in its infancy; only a few large national museums have adopted visual imagery design, while relevant research in small folk art museums is still rare. This paper takes Guangzhou Folk Museum as the research object, based on which a new model of interactive design of Guangzhou Folk Museum display map using intelligent wireless sensor technology is proposed, combining the industrial characteristics of folk art and the visual imagery characteristics of Guangzhou Folk Museum. By analyzing the performance of the core algorithm of this system, it is concluded that the performance of the system is very good. However, the application of this model can enable the Guangzhou Folk Museum to establish the brand image of Guangzhou Folk Museum and create more folk culture value in the face of new opportunities and challenges.

Intelligent Wireless Sensor Networks for Healthcare: Bridging Biomedical Clothing to the IoT Future

The science of achieving a healthy mind, body, and spirit through objectives and activities is known as personal health (PH). We must be aware of our mental, bodily, and social well-being. The term hygiene refers to a wide variety of healthy behaviours. Individuals' healthcare costs and quality of life increased by avoiding or reducing the long-term effects of the disease through knowledge and skills. Biomedical apparel includes sutures, vascular grafts, and biodegradable clothes (BC). Biomedical clothing is anything implanted or incorporated into the human body and used near tissue, blood, or cells. Quick, dependable, and energy-efficient connectivity between wireless sensor networks is necessary (WSNs). Physical layers, media access control, networking layers, and control requirements must be co-designed. For those with lesser means, health insurance will increase in cost. There are difficulties with privacy and cyber security, a higher chance of malpractice claims, and increased time and financial expenditures for doctors and patients. In this study, wireless sensor networks-based personal health biomedical clothing (PH-BC-WSN) was utilized to increase access to high-quality healthcare, increase food production through precision agriculture, and raise the standard of human resources. More effective healthcare and medical asset monitoring systems can be developed thanks to the Internet of Things. Eavesdropping on medical data, modification, fabrication of warnings, denial of service, user tracking and location, physical interference with equipment, and electromagnetic threats were extensively discussed. The article gives several instances of current technology, discusses design challenges including energy efficiency, security, and scalability, provides various demonstrations of current technology, and provides a complete analysis of all the advantages and disadvantages. Despite their many benefits, body sensor networks have several significant obstacles and unresolved research problems, which are described along with some potential answers. As a result, the experimental findings demonstrate that PH-BC-WSN enhances accuracy and reduces response time in inpatient health monitoring.

Design of High-Performance Intelligent WSN based-IoT using Time Synchronized Channel Hopping and Spatial Correlation Model

Wireless Sensor Network (WSN) is one of the most significant contributors to the Internet of Things (IoT), and it plays a significant role in the lives of individuals. There are three main problems in the design of traditional WSN based-IoT. First problem about data; the WSN transmits a huge volume of data to the IoT for processing. The second problem is the energy; since sensor nodes rely on their limited battery, conserving energy is crucial, and the third problem about efficiency of transmission. This paper presents new WSN based IoT framework that integrate important techniques to solve these problems; To increase the effectiveness of data processing and storing, the intelligent Adaptive Boosting stochastic algorithm is applied. IEEE 802.15.4e time slotted channel hopping (TSCH) protocol is used because it has the benefits such as collision-free transmission and multi-hop transmission. Data reduction at the Gateway (GW) level of the network is achieved through spatial correlation between sensors with the goal of conserving energy. Principle idea of this new framework is to identify the advantages of integrating the important techniques; intelligent Adaptive Boosting Stochastic diffusion search algorithm, TSCH, and Special correlation model. As a result, the proposed framework can thereby satisfy the need for a long battery life of low-rate applications and at the same time, the need for high throughput for high-rate uses also for testing it in achieved efficient classification of data, the important performance measures are used.

Open Issues for Intelligent Connectivity Wireless Sensor Networks (WSNs) and IoT: State of the Art

Open Issues for Intelligent Connectivity Wireless Sensor Networks (WSNs) and IoT: State of the Art

Intelligent Fault-Tolerance Data Routing Scheme for IoT-Enabled WSNs

Wireless sensor networks (WSNs) have become one of the essential components of the Internet of Things (IoT). In any IoT application, different sensor-based devices gather data from physical objects and transmit the sensed information to the base station (BS). The BS analyzes this information depending on the sensor location. A high-performance intelligent WSNs is essential for any IoT-based application. In this article, high-performance intelligent WSNs are referred to as IoT-enabled WSNs. In IoT-enabled WSNs, fault occurrence probability is much more than traditional networks. Faulty nodes and broken links affect the reliability of the IoT-enabled WSNs. Various fault-tolerance algorithms enhance the network’s reliability using multipath transmission, relay node placement, and backup node selection. However, these algorithms suffer from huge data transmission delays, packet overhead, and less detection accuracy. In this work, a multiobjective-deep reinforcement-learning (DRL)-based algorithm is proposed for fault tolerance in IoT-enabled WSNs. The main objective of this work is to detect the faulty nodes with high accuracy and less overhead. Furthermore, this work focuses on reliable data transmission after fault detection. Finally, a mobile sink (MS) is used for energy-efficient data gathering that significantly improves the network lifetime. Extensive simulations and theoretical analysis prove that the proposed algorithm outperformed as compared to the state-of-the-art algorithms in terms of fault detection accuracy (FDA), false alarm rate (FAR), false-positive rate (FPR), network lifetime, and throughput.

Signature based Merkle Hash Multiplication algorithm to secure the communication in IoT devices

Great extent of modernization in intelligent device automation and wireless sensor network have encouraged the augmentation of IoTs to interconnect millions of actual devices to the Internet with the help of sensors, actuators and communication technologies. IoT devices with the potential of pervasive sensing and quantifying ability, it becomes an essential segment of the future Internet. The sixth generation (6G) enabled IoT networks are predicted to transform the data sharing and communication for a future of thorough brilliant and sovereign systems. However, secure communication in a sensor network is very difficult and is more amplified by the use of latest 6G IoT technology which is vulnerable to different kinds of attacks due to their limited security features. Existing security systems like RSA and Elliptic curve cryptography which is based on large integer factors and the discrete logarithmic problem is vulnerable to the attack which emerges from quantum computers. The computing capacity of a quantum computer is higher than classical computers. A 30-qubit quantum computer has the equivalent computing capacity as a traditional silicon-based computer computes at 10 teraflops per second. To secure the communication in the present and future IoT network, an enhanced Hash-based Post-Quantum Cryptography (PQC) architecture named Signature-based Merkle Hash Multiplication (SMHM) algorithm is proposed. In the proposed scheme, the Hash-based Merkle signature algorithm is enhanced using the Bernoulli Karatsuba Multiplication algorithm to secure the data storage and communication in IoT devices. The FPGA based proposed model is implemented in the Xilinx ISE14.5 simulator, both Hardware Descriptive Language (HDL) and High-Level Synthesis (HLS) approach is used to calculate the performance metrics which improved 33% frequency, 22% area, power consumption up to 13%, 10% error and 15% delay.

Power Optimization for Aerial Intelligent Reflecting Surface-Aided Cell-Free Massive MIMO-Based Wireless Sensor Network

Intelligent reflecting surfaces (IRSs) have significant advantages in enhancing the coverage and reducing the deployment cost of wireless networks. This paper studies an aerial IRS- (AIRS-) enhanced cell-free massive multiple-input multiple-output- (MIMO-) based wireless sensor network (WSN) in which multiple access points (APs) serve several sensor users (SUs). Direct links between the APs and SUs are blocked due to occlusion by tall buildings. Hence, we deploy an AIRS to improve the communication quality of the SUs. Our goal is to minimize the total transmit power of all APs under a given minimum signal-to-interference-plus-noise ratio (SINR) requirement. We propose a joint iterative optimization algorithm by designing an active beamforming mechanism at each AP and a passive beamforming mechanism at the AIRS to solve this problem. Simulation results illustrate the good performance of the proposed method.

Artificial Intelligence Technique and Wireless Sensor Networks in Energy Management System for Secure Power Optimization

The modern basic building blocks of a control system consist of data acquisition, dispensation of data by the system operators and the remote control of system devices. However, the physical controls, technical examinations and deductions were originally implemented to aid the process and control of power system design. The complexity of the power system keeps increasing due the technical improvements, diversity and dynamic requirements. Artificial intelligence is the science of automating intelligent activities presently attainable by individuals. Intelligent system techniques may be of excessive benefit in the application of area power system controls. Whereas smart grid can be measured as a modern electric power grid structure for better productivity and dependability via automatic control, excessive power converters, modern communications setup, sensing and metering equipment, and modern energy management techniques established on the optimization of demand, energy and network accessibility,and so on. The enormous depiction of the entire transmission grid, in the perspective of smart grids, is quite unclear; and in Nigeria no studies have been put on ground in order for the existing network to be turn into a smart grid. In this research work emphasis is placed on generation and transmission stations; power optimization using artificial intelligent techniques and wireless sensor networks for power control management system.

An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high‐yield and low‐energy‐consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road and use the self‐organizing capabilities of each node to automatically establish the entire network. In the large‐scale self‐organization process, the importance of tasks undertaken by each node is different. It is not difficult to prove that the task allocation of traffic remote sensing sensors is an NP‐hard problem, and an efficient task allocation strategy is necessary for the ITWSNs. This paper proposes an improved adaptive clone genetic algorithm (IACGA) to solve the problem of task allocation in ITWSNs. The algorithm uses a clonal expansion operator to speed up the convergence rate and uses an adaptive operator to improve the global search capability. To verify the performance of the IACGA for task allocation optimization in ITWSNs, the algorithm is compared with the elite genetic algorithm (EGA), the simulated annealing (SA), and the shuffled frog leaping algorithm (SFLA). The simulation results show that the execution performance of the IACGA is higher than EGA, SA, and SFLA. Moreover, the convergence speed of the IACGA is faster. In addition, the revenue of ITWSNs using IACGA is higher than those of EGA, SA, and SFLA. Therefore, the proposed algorithm can effectively improve the revenue of the entire ITWSN system.

The network of telecommunications cross‐layer cooperation control of wireless sensor communication network applied in smart distribution grid

SummaryWith the rapid development of artificial intelligence, the intelligent distribution network can effectively improve the overall power supply quality of the distribution system. The designed distributed power has been able to achieve an optimal operation of grid connection, while the high‐speed operation of distribution equipment can achieve effective interaction with users. At present, intelligent distribution network is widely studied and applied in power industry because of its advantages of high efficiency energy and environmental protection. Because of its unique characteristics, such as low cost and fast speed, wireless sensor network is cleverly applied in intelligent distribution network. However, wireless sensor network has some defects in energy utilization due to its large range and too many nodes. This paper presents a cross‐layer cooperative control model, which can control the real time and reliability of network power data communication through stack protocol layer. This method can learn the weight value adaptively by training the influence relationship between the parameters of each protocol layer and achieve real‐time and reliable data communication by adjusting the policy and parameters of each layer in real time. This method takes the parameters of each wireless sensor node as the unit to establish the influence relationship between each other so as to realize the connection between each node of wireless sensor network and then to control the wireless sensor network as a whole to ensure the real‐time reliability of data communication in intelligent distribution network. In this paper, a lot of simulation experiments have been done, and the results show that the proposed cross‐layer cooperative control model can control the whole wireless sensor network to achieve efficient data transmission capacity of intelligent distribution wireless sensor network.

A Multi-Node Detection Algorithm Based on Serial and Threshold in Intelligent Sensor Networks.

With the continuous progress of science and technology, intelligent wireless sensor network (IWSN) communication has become indispensable in its role in production and life because of its convenient network settings and flexible use. However, with the widespread availability of intelligent wireless sensor networks, the use of many wireless sensor nodes constitutes a multi-node wireless communication system, which turns the accuracy and low complexity of multi-node detection in sensor networks into a problem. Although the traditional algorithm has excellent performance, it cannot give consideration to both accuracy and complexity. Therefore, a maximum logarithm message passing algorithm based on serial and threshold (S-T-Max-log-MPA) for multi-mode detection in IWSN is proposed in this paper. In this algorithm, the threshold is used to determine the necessary conditions of sensor node stability first, and then the sensor node information updating is integrated into the resource node information updating, so that the system can maintain good accuracy, performance, and change the situation of poor system accuracy at low threshold. Compared with the traditional algorithm, the proposed algorithm significantly changes the algorithm complexity reduction rate of the system multi-node detection. Simulation results show that the algorithm has a good balance between accuracy and complexity reduction rate.