Sensor Network Model Research Articles

Periodic orbit analysis for a delayed model of malicious signal transmission in wireless sensor networks with discontinuous control

This paper employs a discontinuous temporary immunity control to obtain the periodic orbit for a class of delayed malicious signal transmission model in wireless sensor networks under the framework of differential inclusion. The positivity and boundedness of the solution for the discontinuous system is proved first. Then, by using the Kakutani's fixed point theorem of set‐valued maps, the existence of a periodic orbit is obtained under some assumptions and constraints. Furthermore, the globally exponentially stable ‐periodic orbit is investigated using the Lyapunov functional method. The obtained results can help us better understand the dynamic characteristics of discontinuous delayed systems and have direct applications to the wireless sensor networks for guaranteeing fast response to malicious signals. Finally, the numerical simulations of three examples are given to validate the correctness of the theoretical results.

Self-improved butterfly optimization algorithm based cooperative routing model in Wireless Multimedia Sensor Networks

Due to its potential influence, Wireless Multimedia Sensor Networks (WMSNs) receive a lot of attention from numerous appealing applications and the scientific studies that surround them. Over the past few years, the most active area of the research field has been routing in WMSNs. Additionally, the routing protocol is used to transfer the multimedia data that identifies the most effective resources and stable paths, as well as to guarantee various degrees of Quality-of-Experience (QoE) and Quality Of Service (QoS). It remains a difficult aspect, nevertheless, due to the complex responsibilities of multimedia, dynamic variations in network state, and a shortage of network resources. In order to choose the most appropriate cooperative nodes, this research seeks to implement a Deep Belief Network (DBN) based decision-making system. In addition, restrictions such as Tri-level energy consumption of nodes (Level 1: Low energy level for textual data, Level 2: Mid energy level for text, image, and video information, Level 3: Highly energetic level for high definition pictures), (ii) Reliability, and (iii) Delay are investigated while transferring the multimedia data in the network. Self-Improved Butterfly Optimization Algorithm (SIBOA) is proposed to tune the optimal weight of the DBN in order to make the correct choice. Finally, a comparison of the selected scheme's performance to various conventional schemes is made in terms of alive nodes, delay, residual energy, network lifetime, and reliability analysis.

A Novel Machine Language-Driven Data Aggregation Approach to Predict Data Redundancy in IoT-Connected Wireless Sensor Networks

Real world data aggregation and delivery in Internet of Things (IoT) technology are essential to predict and retrieve target data in short time so that the end user feels no delay but ensures a high quality of information. In addition to habitat monitoring and disaster management, these networks have a wide range of other uses, including security and military operations. The processing capabilities of sensor nodes are restricted due to the fact that they have a limited battery life and hence a modest size and processing capacity. WSNs are also susceptible to failure as a result of the limited battery power available. In WSNs, data aggregation is practiced as an energy efficient strategy to reduce computing and transmission latency. It is because of sensor node distribution density that shares the same data at a time data redundancy comes to exist. It is possible to reduce redundancy by adopting a suitable machine learning algorithm while executing the data aggregation process. Researchers are still chasing behind algorithms and modeling strategies effectively to ease the process of developing an effective and acceptable data aggregation strategy from existing wireless sensor network (WSN) models. A three stage framework is proposed for an efficient data aggregation mechanism, and the stages are Modified LEACH, extreme learning machines (ELM), adaptive Kalman filter, and Bi-LSTM. This experiment result shows better performance than the existing methods.

Design and implementation of enhanced security model for wireless sensor network on ARM processor

Due to the radio range of the network; suspicious transmission; unattended nature; and easier access; Wireless Sensor Networks are vulnerable to malicious users and physical attacks. Hence security is a must in these cases. The development of a WSN for a secured temperature measurement system could be a feasible answer to these issues. The temperature signals are collected by sensors and transmitted to an Advanced RISC Microprocessor (ARM) via Wireless Fidelity (Wi-Fi) technology, which has been proven to transfer data accurately and reliably. The data is then stored in memory that is controlled by the microprocessor. The functionality is created using an ARM 9-based Samsung S3C2440 Controller with the Linux operating system. The security system is incorporated using the host MCUs and the temperature values are converted to digital form using the ARM processor's ADC (S3C2440). The processor is connected to the console terminal through UART, which sends data to the system on a regular basis with security data being monitored and transferred to other Wi-Fi-equipped devices through a USB-based Wi-Fi module, and the temperature readings are continuously monitored via this wireless sensor network. We perform the validation of our study through MATLAB simulations. Through MATLAB simulations we measure the total energy dissipated, throughput, and the lifetime of the sensor nodes. Overall, in this research, we develop an improved security in WSN system with application in ARM controller based secured temperature monitoring system. This paper paves a direction towards further research in physical security in Wireless Sensor Networks.

Energy Enhancement of WSN with Deep Learning based SOM Scheduling Algorithm

Energy efficiency is one of the primary requirements for designing a successful Wireless Sensor Network (WSN) model. The WSN systems are generally made with a group of nodes that are operated with a small size battery device. To improve the energy efficiency of such WSNs several methodologies like clustering approach, mobile node technique and optimal route planning designs were developed. Scheduling method is yet an efficient model that is widely used in WSN applications, that allows the nodes to be operated only for a certain prescribed time. The proposed work utilizes the Self Organizing Maps (SOM) approach for improving the performances of the scheduling algorithms to a certain limit. SOM is a kind of artificial neural network that analyzes the problem based on competitive learning rather than the backpropagation methods. The work compares the proposed algorithm with the traditional Ant Colony and Software Defined Network approaches, wherein the proposed approach has shown an improvement in terms of energy conservation and network lifetime.

An outlier detection method based on the hidden Markov model and copula for wireless sensor networks

An outlier detection method based on the hidden Markov model and copula for wireless sensor networks

User Experience Perspectives on the Application of Interactivity Design Based on Sensor Networks in Digital Museum Product Display

This paper designs a model of interactivity design in digital museum product display, constructs a sensor network model, and tests the sensor network-based interactivity design in digital museum product display under the perspective of user experience. This paper makes a museum user experience model based on sensory, behavioral, cognitive, and emotional experiences; establishes a user experience design framework; conducts specific theoretical analysis and research from four aspects; uncovers specific factors affecting museum user experience; analyzes the impact of each experience factor on user system design and possible design entry points; and proposes corresponding user system design strategies to guide subsequent design practice sessions. This paper also completes the hardware circuit and PCB board design of the wireless sensing node based on the CC2430 chip as the core. Tomcat WEB server and J2EE-based Spring MVC Web development framework are used as the leading implementation technology to complete the functions of real-time data query, network parameter setting, and historical data storage. The ideographic practice of visual representation of the digital museum is discussed around its optical system. Knowledge-based model dominates the digital museum representation, and examining the complex constitutive processes and characteristics of images involves not only the production of discursive order but also the production of presentation contexts as well as virtual spaces, which construct a new visual presentation of digital museums. However, the virtual representation practice of digital museums still has paradoxes, the absence of the sense of experience and the flatness of the virtual space, in which the audience cannot have a visual experience under this presentation.

TOA-Based Relative Localization With Reflecting Signal Paths Assisted: Algorithm and Performance Analysis

This letter investigates a communication node relative localization problem based on time-of-arrival (TOA), where both the distances in direct signal paths and reflecting signal paths are measured. A wireless sensor network model of distance measurements and node locations is established, and a new two-step localization iterative algorithm is proposed. Simulation results illustrate the performance of the proposed algorithm, which is compared with Cramer–Rao lower bounds (CRLBs) and genetic algorithm (GA)-based algorithms.

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.

Comparison of 920 MHz and 2.4 GHz Near Ground Electromagnetic Wave Pathloss Propagation Model for Wireless Sensor Network in Forest Environment Application

A Wireless Sensor Network (WSN) system that uses wireless communication technologies occasionally experiences data loss when undertaking wireless data communication. This problem happens because of the WSN system being placed on top of the ground and surrounded by vegetation that adds more loss to the transmission. In order to avoid this problem, the wireless system design must meet its best performance. To build the best performing WSN system, electromagnetic wave behavior in the forest environment needs to be studied well. This paper investigates the electromagnetic wave behavior transmitted and propagated by a WSN node at less than 30 cm from the ground using a 920 MHz frequency. We have analyzed that low height (30 cm) and vegetation environments can also add more loss at about 30.96 dB to the free space pathloss model. The new 920 MHz (that adds 30.96 dB loss) model shows identical behavior to 2.4 GHz with an average difference of 12.24 dB. However, the 920 MHz model performs better, achieving an average RMSE of 1.06 compared with the 2.4 GHz model, which can only achieve an average RMSE of 4.92 compared with the 920 MHz measurement.

Self-Healing and Shortest Path in Optical Fiber Sensor Network

In this study, a new square-based fiber Bragg grating (FBG) sensor network model is proposed to address possible link failures in FBG sensor networks and improve their reliability. Graph theory and optical switching are simultaneously applied to these sensor networks to improve their self-healing ability; the FBG sensor network is regarded as a directed graph. Three commonly used self-short-circuit algorithms are compared in terms of the self-healing capabilities that they provide to the optical fiber sensor network. Among these, the shortest-path faster algorithm achieved a high, nearly 90% repair accuracy and had an average repair time of 0.103 s, the shortest in this study. The newly designed FBG self-healing network can be reorganized and repaired when local damage occurs, thereby improving its reliability.

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.

An energy-balanced head nodes selection scheme for underwater mobile sensor networks

Underwater sensor networks system is a promising technology for smart ocean monitoring applications. To address the imbalance problem of network overhead and limited energy of underwater sensor nodes, this paper presents a CH–SH Selection and Sink Path-planning (CSSP) scheme to collect vast amount of data from heterogeneous sensor nodes. The scheme firstly establishes a three-layer network structure model for underwater mobile sensor networks (UMSN) based on multi-mode communication mechanism and provides energy-balanced head nodes (cluster head and sub-cluster head nodes) selection algorithm based on particle swarm iterative optimization. Then, the optimal global-path-plan of mobile sink is proposed to visit all the head nodes to collect data, reduce the multi-hop underwater acoustic transmission distance of relay nodes, and avoid the energy hole problem. Lastly, the upper-layer multi-hop network of UMSN is designed to remotely control local-path-plan over mobile sink, in order to implement joint planning of global and local paths of mobile sink. Simulation results verified that the proposed CSSP scheme outperformed 11%, 16% and 22% over three typical protocols in terms of nodes energy consumption, CSSP was 9%, 12% and 19% lower than three typical protocols in terms of SD of energy consumption, packet delivery ratio of CSSP was 8%, 10% and 12% higher than three typical protocols. The scheme could significantly balance energy and reduce packet loss rate.

Advanced Intelligent Big Data Analytic-Based Automated Layout for Environmental Design Intelligent Sensor Networks

In this paper, through the in-depth study of advanced intelligent extensive data analysis and the design of the model of the environment design automatic layout intelligent sensor network, we realize the environment design intuitive layout smart sensor network based on advanced thoughtful and extensive data analysis. According to the non-beacon-enabling CSMA/CA algorithm of the MAC layer of a single node in intelligent sensor network, the analysis model of the node MAC layer of the innovative sensor network based on the Markov chain is established. The mathematical calculation method of node MAC layer delay is obtained through the analysis and calculation of nodes’ Markov state transfer process in this model. By analyzing the internal composition of design grooming, data support, analysis modeling, and platform in environmental design big data and the relationship between each part, the industrial extensive data analysis modeling method system is formed based on the CRISP-DM model, based on which the demand analysis of the industrial comprehensive data analysis modeling platform is carried out, and the overall architecture of the platform is formed; this paper proposes an automatic layout algorithm based on strategy iteration for each. Each type of environment design is modeled, and the layout scheme is sequentially generated. The applicability, aesthetics, and comfort are considered, but the design information provided by the space contour can be regarded to develop a reasonable layout scheme. The experimental results show the rationality and versatility of the automatic layout schemes, proving the practicality and time-saving nature of the work.

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.

Probabilistic Buckshot-Driven Cluster Head Identification and Accumulative Data Encryption in WSN

Several nonterminal nodes in the ad-hoc sensor network architecture are involved in effectively communicating data. There are not enough nodes other than the terminals to process sensor data and send it between nodes. Because of this, the exchange of sensor data relies on devices capable of predicting events and responding quickly. Identifying the cluster head is essential to the network’s long-term viability and operational efficiency. This paper proposes a robust probabilistic buckshot approach to identify the appropriate nodes, and the smooth handover mechanism in the corresponding cycles is mechanized. The proposed model also employs a heuristic algorithm named HARIS to identify the best cluster head by analyzing the residual energy associated with each sensor node over multiple iterations. The data exchanged among the nodes is encrypted using a lightweight accumulative data encryption model to ensure the confidentiality of the data. The proposed model is evaluated using various statistical analysis metrics like node availability, computational delay, throughput, and network lifetime. The proposed model outperforms the existing energy-sensitive sensor network models by 20–23%.

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.

MmWave channel model for intra-vehicular wireless sensor networks

Intra-vehicular wireless sensor networks (IVWSNs) have significant potential to reduce part, manufacturing and repair costs, facilitate the integration of new nodes and provide more freedom for the sensor placement in previously impossible locations by obviating the need for wiring harness. mmWave stands up as a promising candidate to fulfill the high reliability, security and low latency requirements of IVWSNs, exploiting the availability of large bandwidth at high frequencies and high nominal gains with directional antennas while attenuating through the car body significantly. This work focuses on building a mmWave channel model for IVWSNs. The model is built for the engine compartment, passenger compartment and beneath the chassis of a vehicle by conducting vast number of measurements for 14 × 14, 13 × 13 and 15 × 15 transmitter and receiver links in a Fiat Linea, respectively. The path loss exponent is approximately 3, showing almost no variation within different compartments. The power variation around the path loss model has a Generalized Extreme Value (GEV) distribution with zero mean for all compartments and 5 dB standard deviation for the engine compartment and approximately 7.6 dB standard deviation for the other two compartments. A modified Saleh–Valenzuela (SV) model is used to represent the clustering of power delay profiles (PDPs). Log-normal distribution is used to model the inter-arrival times of clusters, while the dependencies of cluster amplitude and ray decay rate on the cluster arrival times are represented by a dual slope linear fit model with breakpoints 1.2 ns and 5.6 ns for engine compartment, respectively, and 1.6 ns and 2.6 ns for the other two compartments, respectively. The experimental PDPs vary around the SV model and these variations are represented by a normal distribution with zero mean and 5.8 dB standard deviation, which is independent of both the delay bins and the compartment of the vehicle. All these findings are used to build a simulation model for each compartment of the vehicle. The simulation model is validated by comparing the distributions of the received powers and Root Mean Square (RMS) delay spreads of the experimental and simulated PDPs.

Performance research on ZigBee wireless sensor network self-organizing network for 220 kV four-circuit transmission lines on the same tower

With the integration of massive power transmission equipment and the Internet of Things (IoT), it is particularly important to choose a suitable low-power sensor networking structure, when facing a large amount of on-site sensing data. To select a suitable ZigBee network structure with a large number of embedded sensing nodes, for 220 kV four-circuit transmission lines on the same tower, this paper uses OPNET simulation software to conduct three different ZigBee wireless sensor network (WSN) models, respectively with star, tree and mesh topologies. Then, the performance of the Media Access Control (MAC) layer (delay and throughput performance), application layer (end-to-end delay and traffic received performance), and network layer (number of hops and packets dropped performance) is further investigated and compared for these three different ZigBee WSNs under 220 kV transmission scenario. Considering that quantity of sensing nodes would influence the ZigBee WSN performance greatly, thus, the simulation results under two application scenarios, 220 kV four-circuit transmission lines on the same tower and 500 kV double-circuit transmission lines on the same tower, were thoroughly compared and analyzed in this paper.

Research on the Intrusion Detection Model of Underwater Sensor Networks

The characteristics of underwater nodes, the harshness of the underwater environment, and the openness of the deployment area make underwater sensor networks vulnerable to various network attacks; in order to defend against potential threats and attacks, intrusion detection, as an active defense technology, can detect different attacks before they occur; currently, the technology is less used in underwater sensor networks and has a low detection rate for some types of attacks and cannot effectively detect the problem of multiple attack types. To address this problem, this paper proposes an intrusion detection model for underwater sensor networks for multiple types of attacks. Firstly, cluster head nodes use neighborhood rough sets for feature extraction, and the reduced dimensional data is transmitted to sink nodes to reduce the node computation. Further, the synthetic minority oversampling technique (SMOTE) is used to balance the data set, increase the number of minority class samples, and improve the detection rate of minority class attacks. Finally, determine whether a node is trusted according to the trust value of the cluster head node, and train the classifier using the random forest algorithm to detect the type of attack; it suffers to achieve intrusion detection of multiple types of attacks. Simulation results show that the model can not only improve the performance of intrusion detection of multitype attacks but also achieve an accuracy of over 99% for the detection of imbalance classes.