Wireless Network Model Research Articles

Network Security Situational Awareness Based on Genetic Algorithm in Wireless Sensor Networks

How to design an effective and secure user authentication scheme suitable for complex wireless sensor network (WSN) environments has very important research significance. Based on the genetic algorithm theory, the paper constructs the security situational awareness model of wireless sensor network, and studies the WSN user security authentication scheme. The model uses the genetic algorithm extraction technology combined with the hash function to generate the biometric key, which solves the data accuracy problem of network security situational awareness: the user’s biometric key is obtained through the fuzzy extraction technology; the resistance of the node is to be attacked, but it is powerless when most of the nodes are broken, and it limits the scalability of the network. During the simulation process, the authentication of the identity of the cluster head by the base station is added in the topology establishment stage, and a one-way hash function and a shared key are added to the key management, so that the encryption key and the authentication key change periodically and dynamically, so that enhanced network communication security. The analysis results show that the protocol is feasible. The algorithm proposed in this paper reduces about 70% of the features in the original feature set, the classification detection rate is increased from 95.7212% to 97.7379%, and the false alarm rate is reduced from 7.9427% to 3.1072%, effectively improving the balance between safety and design cost.

Application of Wireless Sensor Network Model Based on Big Data Ecosystem in Intelligent Health Monitoring System

With the continuous enhancement of people’s health awareness, the awareness of disease prevention began to improve, and the demand for intelligent health status monitoring is also increasing. At present, the health status monitoring mostly depends on hospital equipment, which has high cost and cannot realize remote analysis. Based on this, this paper studies and analyzes the application of wireless sensor network model based on big data ecosystem in intelligent health monitoring. ZigBee protocol is used to realize data transmission, wireless sensor is used to monitor human body temperature, heart rate, blood oxygen saturation, and other physiological indicators, human posture monitoring algorithm is used to monitor posture changes, which is combined with human physiological parameter information, and the long-term and short-term memory model is used to effectively use the time sequence information of data to realize the functions of physiological parameter measurement, wireless positioning, and information fusion. An experiment was designed to test the accuracy of the health monitoring system. The results show that the wireless sensor retention model can meet the design requirements in terms of the monitoring accuracy of body temperature, heart rate, and blood oxygen saturation index in intelligent health monitoring, with low cost and can be combined with human dynamic information.

Sensors Energy Optimization for Renewable Energy-Based WBANs on Sporadic Elder Movements.

The world is advancing to a new era where a new concept is emerging that deals with “wirelessness”. As we know, renewable energy is the future, and this research studied the integration of both fields that results in a futuristic, powerful, and advanced model of wireless body area networks. Every new emerging technology does have some cons; in this case the issue would be the usage of excess energy by the sensors of the model. Our research is focused on solving this excessive usage of energy to promote the optimization of energy. This research work is aimed to design a power-saving protocol (PSP) for wireless body area networks (WBANs) in electronic health monitoring (EHM). Our proposed power-saving protocol (PSP) supports the early detection of suspicious signs or sporadic elder movements. The protocol focuses on solving the excessive energy consumption by the body attached to IoT devices to maximize the power efficiency (EE) of WBAN. In a WSNs network, the number of sensor nodes (SNs) interact with an aggregator and are equipped with energy harvesting capabilities. The energy optimization for the wireless sensor networks is a vital step and the methodology is completely based on renewable energy resources. Our proposed power-saving protocol is based on AI and DNN architectures with a hidden Markov model to obtain the top and bottom limits of the SN sources and a less computationally challenging suboptimal elucidation. The research also addressed many critical technical problems, such as sensor node hardware configuration and energy conservation. The study performed the simulation using the OMNET++ environment and represent through results the source rate to power critical SNs improves WBAN’s scheme performance in terms of power efficiency of Sporadic Elder Movements (SEM) during various daily operations.

Coverage analysis and a new metaheuristic approach using the Elfes Probabilistic detection model in Wireless sensor networks

The dynamic deployment of sensors in AoI (Area of Interest) plays an important role in ensuring the quality of services (QoS) of Wireless Sensor Networks (WSNs) by optimising the coverage and lifetime of the network. In this paper, a new dynamic deployment approach using metaheuristics based on Electromagnetism-Like (EM-L) algorithm and Elfes Probabilistic Detection Model (EPDM) was proposed to optimise the coverage of WSNs based on the sensor coverage problem, and coverage analysis of AoI was performed. A variable threshold detection probability (TDP) was defined instead of defining a fixed TDP as in the literature. Thus, a more realistic modelling environment was created by considering the signal-to-noise ratio (SNR) in the coverage calculation. The simulation results show that the sensors are always effectively deployed in different scenarios with variable TDPs by the proposed approach compared to a random distribution.

RETRACTED ARTICLE: Terahertz fading model for wireless nanosensor networks in advanced medical manufacturing technologies

RETRACTED ARTICLE: Terahertz fading model for wireless nanosensor networks in advanced medical manufacturing technologies

Received signal strength prediction model for wireless underground sensor networks using machine learning algorithms

Analysis of the Received Signal Strength (RSS) from the Wireless Underground Sensor Network (WUSN) is essential before the deployment of the sensor network. It is difficult to predict received signal strength from an underground sensor network using a theoretical model due to variations in environmental conditions. Prediction of RSS using the machine learning approach considers varying environmental conditions. In this work, we develop the RSS prediction model using machine learning concepts based on linear regression, Support Vector Machine (SVM) and random forest algorithms. The RMSE and MAE of the machine learning approach based on the SVM algorithm were found to be the least among the three techniques. It is further observed that the RSS prediction using machine learning methods produces lower RMSE than the theoretical model. Accurate estimates of RSS from a machine learning based prediction model help in deciding the node positioning in order to achieve effective communication within a WUSN.

Neveu’s Exchange Formula for Analysis of Wireless Networks With Hotspot Clusters

Theory of point processes, in particular Palm calculus within the stationary framework, plays a fundamental role in the analysis of spatial stochastic models of wireless communication networks. Neveu’s exchange formula, which connects the respective Palm distributions for two jointly stationary point processes, is known as one of the most important results in the Palm calculus. However, its use in the analysis of wireless networks seems to be limited so far and one reason for this may be that the formula in a well-known form is based upon the Voronoi tessellation. In this paper, we present an alternative form of Neveu’s exchange formula, which does not rely on the Voronoi tessellation but includes the one as a special case. We then demonstrate that our new form of the exchange formula is useful for the analysis of wireless networks with hotspot clusters modeled using cluster point processes.

Front-End Model of Wireless Network Combined with Artificial Intelligence in Computer Information Management System

To address the security problem of computer information management, an artificial intelligence- (AI-) based information intrusion detection model is built in combination with wireless network. Firstly, the background and characteristics of wireless local area network (WLAN) technology are analyzed, and the relationship between AI technology and deep learning is introduced. Secondly, an intrusion detection model on account of long short-term memory (LSTM) neural network and gated recursive unit (GRU) is constructed after analysis of different neural network models. The L2 weight attenuation and dropout regularization strategies are combined with the neural network model. Finally, an intrusion detection front-end model combining wireless network and AI is established. From the comparison of intrusion detection experiments, the generalization ability of the model can be improved by using L2 weight attenuation and dropout regularization strategies. Nevertheless, the performance improvement is only slight, so the early stop method is adopted instead of the regularization strategy. Compared with the existing classification models, the overall performance of LSTM and GRU models is improved by about 17%. The performance of GRU model is not much different from that of LSTM model, but the amount of computation is reduced. Therefore, GRU model is the optimal choice to construct intrusion detection system. The intrusion detection models in WLAN and GRU can improve the security performance of computer information management system. To sum up, this work provides reference for the development of computer management system.

Retraction Note to: Energy aware reliable route selection scheme with clustered RP model for wireless sensor networks to promote interaction between human and sensors

Retraction Note to: Energy aware reliable route selection scheme with clustered RP model for wireless sensor networks to promote interaction between human and sensors

The Performance of Artificial Intelligence Translation App in Japanese Language Education Guided by Deep Learning.

With recent technological advances in wireless networks and the Internet, social media has become a vital part of the daily lives of people. Social media like Twitter, Facebook, and Instagram have enabled people to instantly share their thoughts and ideas about a particular topic or person's life. Emotion classification in Twitter data remains a hot search topic in the field of artificial intelligence (AI). Though several models have been developed for tweet data in English, it is still needed to develop an effective tweet emotion classification for the Japanese language. In this aspect, this work develops a new artificial intelligence with an Optimal Long Short-Term Memory-Based Japanese Tweet Emotion Classification (OLSTM-JTCC) model in wireless networks. The proposed OLSTM-JTCC technique aims to examine emotions and categorises them into proper class labels. The proposed OLSTM-JTCC technique initially employs the TF-IFD model for the extraction of feature vectors. Besides, the OLSTM model is used to classify the tweet data into different types of emotions that exist within it. In order to improve the classification capability of the LSTM model, the Henry gas solubility optimization (HSGO) algorithm is applied as a hyperparameter optimizer. The performance validation of the OLSTM-JTCC technique took place using Japanese tweets, and the comparative results highlighted the better performance of the OLSTM-JTCC technique in terms of different measures.

Integrating Stochastic Geometry and ON/OFF Traffic Models: Toward Spatio-Temporal Analysis of Wireless Networks With Heterogeneous Services

Wireless networks (4G, 5G, etc.) of today are witnessing a continuously increasing level of heterogeneity in terms of supported services and applications. Spatio-temporal modeling of wireless networks plays a crucial role, not only in design and analysis but also for critical performance metrics. Therefore, in this work, we develop a joint model for the analysis of wireless networks wherein users can request for different services and can generate multiple flows. By assuming single flow per user for a given service request, we derive the packet arrival probability of the user by using ON/OFF traffic model. Moreover, we derive the probability mass function (pmf) for the number of active users under a base station (BS). By assuming a heterogeneous traffic scenario wherein a user can generate multiple flows for different services, we derive the pmf for the number of active users under a BS. To analyze the performance of wireless network, we derive critical performance metrics which include success probability, mean delay and stable region probability. For computationally efficient implementation of the derived expressions, we also develop a algorithm which takes significantly less time. We benchmark the derived expressions against state-of-the-art literature.

Energy efficient secured cluster based distributed fault diagnosis protocol for IoT

The rapid growth of internet and internet services provision offers wide scope to the industries to couple the various network models to design a flexible and simplified communication infrastructure. A significant attention paid towards Internet of things (IoT), from both academics and industries. Connecting and organizing of communication over wireless IoT network models are vulnerable to various security threats, due to the lack of inappropriate security deployment models. In addition to this, these models have not only security issues; they also have many performance issues. This research work deals with an IoT security over WSN model to overcome the security and performance issues by designing a Energy efficient secured cluster based distributed fault diagnosis protocol (EESCFD) Model which combines the self-fault diagnosis routing model using cluster based approach and block cipher to organize a secured data communication and to identify security fault and communication faults to improve communication efficiency. In addition we achieve an energy efficiency by employing concise block cipher which identifies the ideal size of block, size of key, number of rounds to perform the key operations in the cipher.

Maximizing Energy Efficiency in Hybrid Overlay-Underlay Cognitive Radio Networks Based on Energy Harvesting-Cooperative Spectrum Sensing

Spectrum demand has increased with the rapid growth of wireless devices and wireless service usage. The rapid development of 5G smart cities and the industrial Internet of Things makes the problem of spectrum resource shortage and increased energy consumption even more severe. To address the issues of high energy consumption for spectrum sensing and low user access rate in the cognitive radio networks (CRN) model powered entirely by energy harvesting, we propose a novel energy harvesting (EH)-distributed cooperative spectrum sensing (DCSS) architecture that allows SUs to acquire from the surrounding environment and radio frequency (RF) signals energy, and an improved distributed cooperative spectrum sensing scheme based on energy-correlation is proposed. First, we formulate an optimization problem to select a leader for each channel; then formulate another optimization problem to select the corresponding cooperative secondary users (SUs). Each channel has a fixed SUs cluster in each time slot to sense the main user state, which can reduce the energy consumption of SUs sensing and can reduce the sensing time, and the remaining time can be used for data transmission to improve throughput, and finally achieve the purpose of improving energy efficiency. Simulation results show that our proposed scheme significantly outperforms the centralized scheme in terms of SUs access capability and energy efficiency.

Congestion tracking control for wireless TCP/AQM network via immersion and invariance

AbstractThis paper deals with an immersion and invariance (I&I) control design for wireless Transmission Control Protocol (TCP) network to solve Active Queue Management (AQM) problems. With the help of this strategy, the developed control law can be used to cope with congestion tracking problems for a nonlinear wireless network model. Besides, the nonlinear congestion control law is proposed to assure that the queue length is able to track the desired queue length despite having sudden changes. The developed control design is validated in the MATLAB environment. The effectiveness of the developed control is verified through a simulation and compared with integral backstepping design, backstepping design, sliding mode design, and synergetic control design. The simulation results demonstrated that the presented strategy is not only able to solve the desired congestion tracking control problem but also provide improved transient performances.

Evaluation Method of English Learning Engagement Based on Wireless Communication Network and Big Data

The existing English learning engagement assessment methods still have some deficiencies, which cannot predict students’ interest in English learning, and the teaching effect is not good enough, resulting in the improvement of students’ performance which is not obvious. Therefore, an English learning engagement assessment method based on wireless communication network and big data is proposed. Firstly, build a mobile wireless communication network model, analyze the metaphor processing and evaluation methods adopted by English learners according to big data, and realize the acquisition and interpretation of metaphor meaning through three steps: the establishment of metaphor knowledge base, the analysis and description of metaphor language, and the classification and recognition of metaphor; build an evaluation framework based on wireless communication network and big data, propose a Java application platform for English learning engagement evaluation, and realize the English learning engagement evaluation method. The experimental research shows that the evaluation method has good stability, short evaluation time, and high efficiency and has practical application effect.

On the transition times in a loss model of wireless networks

On the transition times in a loss model of wireless networks

Model-Free design of control systems over wireless fading channels

Wireless control systems rely on wireless networks to exchange information between spatially distributed actuators, plants and sensors. The noise in wireless channels renders traditional control policies suboptimal, and their performance is moreover directly dependent on the allocation of wireless resources between control loops sharing the wireless medium, making the design of control and resource allocation policies critical to achieve reliable performance of the wireless control system. The co-design of control-aware resource allocation policies and communication-aware controllers, however, is a challenging problem due to its infinite dimensionality, existence of constraints on the use of communication resources or performance of individual plants, and need for explicit knowledge of the plants and wireless network models. To overcome those challenges, we propose a constrained reinforcement learning approach to design model-free control-aware communication policies and communication-aware control policies for wireless control systems. We demonstrate the near optimality of control system performance and stability using near-universal policy parametrizations and present a practical model-free algorithm to learn the co-design policy. Numerical experiments show the strong performance of learned policies over baseline solutions.

Stability‐oriented RAN slicing based on joint communication and computation offloading

Network slicing and mobile edge computing are key technologies in 5G/6G networks. Stable allocation of slicing resources is a challenge while communication and computation offloading coexist. To meet the service requirements of communication and computation offloading, a radio access network model which consists of enhanced mobile broadband (eMBB) slicing users, ultra-reliable low latency communication (URLLC) slicing users, base stations integrated with MEC servers, and fog nodes with computing power are proposed. By employing the Lyapunov optimisation technique, a slicing resource allocation algorithm is proposed to maintain the queue stability and minimise the energy consumption of the system. For eMBB and URLLC slices, long time interval and short time interval resource allocation algorithms are proposed respectively. Simulation results show that the proposed algorithm can guarantee the long-term stability of the network system while ensuring the service requirements.

Throughput Analysis for Wireless Full-Duplex Multi-Hop Networks with RTS/CTS-Based MAC

Medium access control (MAC) protocol operations for in-band full duplex multi-hop networks play an important role in efficient data relaying and throughput enhancement. Knowledge of the relationship between essential operations in MAC protocol for full duplex MAC (FD MAC) networks and network performance is important and useful in terms of the protocol and network design. FD MAC protocols often require exchanging control frames, e.g., request to send/clear to send (RTS/CTS). However, the conventional model cannot analyze the performance of wireless multi-hop networks with RTS/CTS-based FD MAC. Thus, this paper proposes a throughput analysis model for wireless multi-hop networks with RTS/CTS-based FD MAC. The proposed model includes novel “airtime expressions”, which allows us to handle RTS/CTS operations under FD MAC. The proposed model provides the end-to-end throughput of multi-hop networks with RTS/CTS-based FD MAC for any number of hops and any payload size. The validity of the analytical expressions is confirmed through comparisons with simulation results.

Research on Channel Modeling and Communication Coverage of Wireless Sensor Networks in Barrier Area of Nuclear Power Plants

In view of the multimetal barrier environment of nuclear power plant, by considering the factors such as transmission power, transmission position, and multipath interference, based on the simulation of metal pipes and equipment, this paper carries out the barrier area channel modeling in logarithmic fading mode and makes quantitative analysis on the channel transmission, path loss, channel power characteristics, and so on under the metal barrier environment. Based on the channel modeling, this paper optimizes the coverage of the network in the obstacle area by using the improved teaching and learning group intelligent algorithm. The simulation results show that the improved teaching and learning algorithm can optimize the network coverage of the obstacle area well, and under the four obstacle modules, 14 nodes can cover the whole area by more than 99%. This provides a solution to the problem of network coverage in the practical application of wireless sensor networks.