Sensor Network Model Research Articles

Malicious attack detection based on continuous Hidden Markov Models in Wireless sensor networks

Alongside the rapid progress of Wireless sensor networks (WSNs) technologies, sensors and networks can rapidly be a victim of distributed attacks. Attackers can perform intrusions to break down the network during the routing process and intercept gathered data by dropping them for example. To prevent the increase of security issues in WSN, many attack identification methods and models were proposed in which detection systems are deployed to collect sensed data and categorize them using machine learning and stochastic binary-classification techniques. For this work, a new method is introduced to analyze and classify an own WSN dataset. We aim to design an anomaly identification approach to improve the sensor network security and its efficiency with high accuracy. To reach this goal, machine learning approaches are used to define a detection system that learns from routing datasets to identify network malicious entries. The proposed models are based on Hidden Markov Model (HMM), and Gaussian Mixture Model (GMM) stochastic assumptions. Also, the dimensionality reduction technique was deployed to select the most relevant features for the training. The experimentation phase was realized on its own and made the dataset that reflects different network situations of normal and attacked cases. The outcomes performances of the proposed method were obtained with a classification accuracy of 92.18% using a 2 HMM/3 GMM classifier. This result demonstrates the quality of our proposed approach compared with existing literature and its usefulness to improve the security of WSN.

A recommendation management defense mechanism based on trust model in underwater acoustic sensor networks

Underwater acoustic sensor networks (UASNs) have been applied in many civilian and military scenarios, but it is vulnerable to various security threats due to the broadcast transmission characteristics and the environment in which they are located. Trust management mechanism has been proven to be an effect way to improve network security. However, in the trust assessment process, some nodes may provide false suggestions, which will lead to trust conflicts and affect the normal operation of the trust mechanism. To screen out unreliable recommendations and dishonest nodes in the network and avoid potential dangers, a recommendation management trust mechanism based on collaborative filtering and variable weight fuzzy algorithm (CFFTM) is proposed in this paper. First, three kinds of evidence of trust: communication-based evidence, data-based evidence, and energy-based evidence are select as indicators. Then the variable weight fuzzy comprehensive evaluation algorithm is applied to calculate the direct trust value of the node. Secondly, in order to quantify the honesty ability of nodes, honesty degree is defined. Then the overall recommendation trust value of the node is obtained using the proposed collaborative filtering algorithm. The simulation results show that the mechanism can well filter out unreliable recommendations and improve the recognition rate and stability of the trust model under typical attack scenarios.

Analysis of the Improvement of Engineering Mechanics Experimental Methods Based on IoT and Machine Learning

With the rapid development of sensor technology, machine learning, and the Internet of Things, wireless sensor networks have gradually become a research hotspot. In order to improve the data fusion performance of wireless sensor networks and ensure network security in the event of external attacks, this paper proposes a wireless sensor optimization algorithm model, involving wireless sensor networks, the Internet of Things, and other related fields. This paper first analyzes the role of the Internet of Things in wireless sensor networks, studies the localization mechanism and hierarchy of the Internet of Things based on wireless sensor networks, and improves the LE-RLPCCA (Position Estimation Robust Local Retention Criteria Correlation Analysis) localization algorithm model based on sensor grids. This paper discusses the problems of machine learning in wireless sensor networks, constructs a sensor-based machine learning model, and designs a data fusion algorithm for a wireless sensor networks' machine learning model. The application of wireless sensors in engineering mechanics experiments is summarized, and the optimization algorithm model of the wireless sensor in engineering mechanics experiments is proposed. The analysis results show that the average accuracy of the DKFCM-FSVM (Density aware Kernel-based Fuzzy C-means Clustering algorithm Fuzzy Support Vector Machine) algorithm in detecting five behaviors is 0.997, 0.992, 0.904, 0.996, and 0.946, respectively, and the accuracy in detecting different behaviors is the best, 0.005, 0.01, 0.003, and 0.006 respectively. It achieves the lowest false positive rate in the detection of different behaviors, and the average false positive rate is 0.004, 0.003, 0.003, 0.008, and 0.005, respectively, which shows that the DKFCM-FSVM algorithm model of wireless sensor networks in engineering mechanics experiments is the optimal solution. The work of this paper has good reference value for the application of wireless sensor networks and the optimization of engineering mechanics experimental methods and is helpful for further research of sensor technology.

Timely reliability modeling and evaluation of wireless sensor networks with adaptive N-policy sleep scheduling

As an energy-efficient strategy, node sleep scheduling has become a hot topic of the energy conservation in wireless sensor networks (WSNs). Among these strategies, the N-policy sleep scheduling considers the energy consumption of node switching, thus effectively reducing the total energy consumption. However, node sleep will also increase the transmission packet delay, which cannot meet the timely reliability requirements of the network. To deal with this problem, we propose an adaptive N-policy sleep scheduling for sensor nodes. Next, in order to analyze the energy consumption and the packet delay under the proposed sleep scheduling, we establish the N-policy M/M/1/C queueing model with waiting time for heterogeneous sensor nodes, then derive the steady-state probability and obtain the performance metrics. Based on this, we establish the energy consumption model of sensor nodes and the delay model of packets. Besides, we propose a timely reliability evaluation model for heterogeneous WSNs, and the timely reliability is estimated by using the Monte Carlo simulation method. Finally, numerical experiments and a real case are performed to demonstrate the proposed sleep scheduling outperforms the N-policy sleep scheduling on delay, energy, and timely reliability.

Optimization of energy and delay on interval data based graph model of wireless sensor networks

Optimization of energy and delay on interval data based graph model of wireless sensor networks

Assessment of aerosol persistence in ICUs via low-cost sensor network and zonal models

The COVID-19 pandemic raised public awareness about airborne particulate matter (PM) due to the spread of infectious diseases via the respiratory route. The persistence of potentially infectious aerosols in public spaces and the spread of nosocomial infections in medical settings deserve careful investigation; however, a systematic approach characterizing the fate of aerosols in clinical environments has not been reported. This paper presents a methodology for mapping aerosol propagation using a low-cost PM sensor network in ICU and adjacent environments and the subsequent development of the data-driven zonal model. Mimicking aerosol generation by a patient, we generated trace NaCl aerosols and monitored their propagation in the environment. In positive (closed door) and neutral-pressure (open door) ICUs, up to 6% or 19%, respectively, of all PM escaped through the door gaps; however, the outside sensors did not register an aerosol spike in negative-pressure ICUs. The K-means clustering analysis of temporospatial aerosol concentration data suggests that ICU can be represented by three distinct zones: (1) near the aerosol source, (2) room periphery, and (3) outside the room. The data suggests two-phase plume behavior: dispersion of the original aerosol spike throughout the room, followed by an evacuation phase where "well-mixed" aerosol concentration decayed uniformly. Decay rates were calculated for positive, neutral, and negative pressure operations, with negative-pressure rooms clearing out nearly twice as fast. These decay trends closely followed the air exchange rates. This research demonstrates the methodology for aerosol monitoring in medical settings. This study is limited by a relatively small data set and is specific to single-occupancy ICU rooms. Future work needs to evaluate medical settings with high risks of infectious disease transmission.

Energy Efficient Routing Protocol for an IoT-Based WSN System to Detect Forest Fires

Around a third of the world’s surface area is covered with forests. This is about 4–5 billion hectares of land. We are losing an additional 3 million hectares of forest cover each year than we did in 2001. Hence, researchers worldwide have been working on the identification and prevention of forest fires more aggressively. The wireless sensor network technology for forest fire detection was established as a result of the shortcomings of standard forest fire detection techniques in real time. This paper proposes an energy-aware Internet of Things (IoT) based Wireless Sensor Network (WSN) model with an efficient clustering and routing technique for forest fire detection. The proposed model is named Energy Efficient Routing Protocol (EERP). The model decreases the energy utilization in sensor nodes by minimizing idle listening in cluster heads. EERP also minimizes the transmission of redundant data by allowing only the sensor nodes close to an event to report the event. Furthermore, the model ensures that sensor nodes with low energy levels do not become cluster heads. EERP uses multi-hop routes to send data from source nodes to the Base Station. The performance of EERP is compared with that of some existing Medium Access Control (MAC) protocols in various scenarios. The simulation results prove that the proposed model reduces energy consumption in sensor nodes significantly.

Sensor Clustering Using a K-Means Algorithm in Combination with Optimized Unmanned Aerial Vehicle Trajectory in Wireless Sensor Networks.

We examine a general wireless sensor network (WSN) model which incorporates a large number of sensors distributed over a large and complex geographical area. The study proposes solutions for a flexible deployment, low cost and high reliability in a wireless sensor network. To achieve these aims, we propose the application of an unmanned aerial vehicle (UAV) as a flying relay to receive and forward signals that employ nonorthogonal multiple access (NOMA) for a high spectral sharing efficiency. To obtain an optimal number of subclusters and optimal UAV positioning, we apply a sensor clustering method based on K-means unsupervised machine learning in combination with the gap statistic method. The study proposes an algorithm to optimize the trajectory of the UAV, i.e., the centroid-to-next-nearest-centroid (CNNC) path. Because a subcluster containing multiple sensors produces cochannel interference which affects the signal decoding performance at the UAV, we propose a diagonal matrix as a phase-shift framework at the UAV to separate and decode the messages received from the sensors. The study examines the outage probability performance of an individual WSN and provides results based on Monte Carlo simulations and analyses. The investigated results verified the benefits of the K-means algorithm in deploying the WSN.

Sensor Deployment Optimization Methods in Electromagnetic Field Based on EEIF-PLI

Electromagnetic field has a great impact on the performance of wireless sensor networks (WSNs). The influence of electromagnetic field generated by power equipment on wireless sensor (WS) nodes is designated as two parameters: environmental electromagnetic interference factor (EEIF) and path loss index (PLI). EEIF is used to describe the probability in the sensor node probability sensing coverage model. PLI is substituted into a node communication path loss model; then, a node packet receiving success rate model can be obtained by above models, which is used to evaluate node communication quality in electromagnetic interference environment. Combined with a service cost model of wireless sensor networks in complex electromagnetic environment, the performance evaluation function of wireless sensor networks is established, and constraints are determined. Solved by the particle swarm optimization (PSO)-gray wolf optimizer (GWO) algorithm, optimal deployment of wireless sensor networks in complex electromagnetic environment is realized. Experimental results show that the proposed method can achieve best performance.

Quality of Service (QoS) Performance Analysis in a Traffic Engineering Model for Next-Generation Wireless Sensor Networks

Quality of Service (QoS) refers to techniques that function on a network to dependably execute high-priority applications and traffic reliably run high-priority applications and traffic even when the network’s capacity is limited. It is expected that data transmission over next-generation WSNs (Wireless Sensor Networks) 5G (5th generation) and beyond will increase significantly, especially for multimedia content such as video. Installing multiple IoT (Internet of Things refers to the network of devices that are all connected to each other) nodes on top of 5G networks makes the design more challenging. Maintaining a minimal level of service quality becomes more challenging as data volume and network density rise. QoS is critical in modern networks because it ensures critical performance metrics and improves end-user experience. Every client attempts to fulfill QoS access needs by selecting the optimal access device(s). Controllers will then identify optimum routes to meet clients’ core QoS needs in their core network. QoS-aware delivery is one of the most important aspects of wireless communications. Various models are proposed in the literature; however, an adaptive buffer size according to service type, priority, and incoming communication requests is required to ensure QoS-aware wireless communication. This article offers a hybrid end-to-end QoS delivery method involving customers and controllers and proposes a QoS-aware service delivery model for various types of communication with an adaptive buffer size according to the priority of the incoming service requests. For this purpose, this paper evaluates various QoS delivery models devised for service delivery in real time over IP networks. Multiple vulnerabilities are outlined that weaken QoS delivery in different models. Performance optimization is needed to ensure QoS delivery in next-generation WSN networks. This paper addresses the shortcomings of the existing service delivery models for real-time communication. An efficient queuing mechanism is adopted that assigns priorities based on input data type and queue length. This queuing mechanism ensures QoS efficiency in limited bandwidth networks and real-time traffic. The model reduces the over-provisioning of resources, delay, and packet loss ratio. The paper contributes a symmetrically-designed traffic engineering model for QoS-ensured service delivery for next-generation WSNs. A dynamic queuing mechanism that assigns priorities based on input data type and queue length is proposed to ensure QoS for wireless next-generation networks. The proposed queuing mechanism discusses topological symmetry to ensure QoS efficiency in limited bandwidth networks with real-time communication. The experimental results describe that the proposed model reduces the over-provisioning of resources, delay, and packet loss ratio.

Data fusion method for wireless sensor network based on machine learning

In order to improve the energy consumption balance between wireless sensor nodes and reduce the energy consumption of nodes in the process of data fusion, a machine learning based data fusion method for wireless sensor networks is proposed. Through the establishment and training of wireless sensor network model, the compressed sensing method is used to collect wireless sensor network data, and the multi-dimensional de aggregation class analysis algorithm is used to de duplicate the collected data. Using the spatial correlation between the data collected by multiple sensor nodes, the DCS method is used to process the abnormal data of WSN network. In order to eliminate the influence of measurement error on the fusion accuracy, the WSN network data is preliminarily fused by combining the adaptive theory with the batch estimation fusion algorithm. Based on the preliminary fusion results of WSN network data, the Bayesian inference method in machine learning algorithm is used to further fuse WSN network data. The experimental results show that the number of surviving nodes is large and the energy consumption is low when using this method for data fusion. The energy consumption between wireless sensor nodes has a certain balance, which proves that this method has a good data fusion effect.

An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning

An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning

Efficient Energy and Delay Reduction Model for Wireless Sensor Networks

In every network, delay and energy are crucial for communication and network life. In wireless sensor networks, many tiny nodes create networks with high energy consumption and compute routes for better communication. Wireless Sensor Networks (WSN) is a very complex scenario to compute minimal delay with data aggregation and energy efficiency. In this research, we compute minimal delay and energy efficiency for improving the quality of service of any WSN. The proposed work is based on energy and distance parameters as taken dependent variables with data aggregation. Data aggregation performs on different models, namely Hybrid-Low Energy Adaptive Clustering Hierarchy (H-LEACH), Low Energy Adaptive Clustering Hierarchy (LEACH), and Multi-Aggregator-based Multi-Cast (MAMC). The main contribution of this research is to a reduction in delay and optimized energy solution, a novel hybrid model design in this research that ensures the quality of service in WSN. This model includes a whale optimization technique that involves heterogeneous functions and performs optimization to reach optimized results. For cluster head selection, Stable Election Protocol (SEP) protocol is used and Power-Efficient Gathering in Sensor Information Systems (PEGASIS) is used for driven-path in routing. Simulation results evaluate that H-LEACH provides minimal delay and energy consumption by sensor nodes. In the comparison of existing theories and our proposed method, H-LEACH is providing energy and delay reduction and improvement in quality of service. MATLAB 2019 is used for simulation work.

Analytical modeling of attacks in wireless sensor networks for attack detection

The article comprises issues of analytical modeling of attacks in wireless sensor networks. A proposed attack model specifies possible types of unauthorized influences at the network level aimed at compromising network nodes, data transmitted over the network, and information services provided by the wireless sensor network. A developed graph-based analytical model of a wireless sensor network aims at detecting network layer attacks by processing actual events received from network nodes and deciding whether the network is running under an attack. The correctness of the models is confirmed by their testing by using an example of a scenario for detecting a wormhole attack aimed at disrupting routing processes in a wireless sensor network.

Corrections to “Multiple Mobile Charger Charging Strategy Based on Dual Partitioning Model for Wireless Rechargeable Sensor Networks”

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , we did not check the article carefully enough, which resulted in writing errors in some parts of the paper. In <xref ref-type="table" rid="table1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Table 1</xref> of the original article, we incorrectly wrote the default charging efficiency of MC as 300 mJ/s, but the correct charging efficiency is 3000 mJ/s. Also, in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 8</xref> of the original article, we incorrectly wrote the coordinate range of X-axis as 50 mJ/s–500 mJ/s, while the correct coordinate range is 500 mJ/s–5000 mJ/s. These errors are writing errors and have no impact on the core content of the article, such as experimental methods and conclusions.

Metaheuristic Secure Clustering Scheme for Energy Harvesting Wireless Sensor Networks

Recently, energy harvesting wireless sensor networks (EHWSN) have increased significant attention among research communities. By harvesting energy from the neighboring environment, the sensors in EHWSN resolve the energy constraint problem and offers lengthened network lifetime. Clustering is one of the proficient ways for accomplishing even improved lifetime in EHWSN. The clustering process intends to appropriately elect the cluster heads (CHs) and construct clusters. Though several models are available in the literature, it is still needed to accomplish energy efficiency and security in EHWSN. In this view, this study develops a novel Chaotic Rider Optimization Based Clustering Protocol for Secure Energy Harvesting Wireless Sensor Networks (CROC-SEHWSN) model. The presented CROC-SEHWSN model aims to accomplish energy efficiency by clustering the node in EHWSN. The CROC-SEHWSN model is based on the integration of chaotic concepts with traditional rider optimization (RO) algorithm. Besides, the CROC-SEHWSN model derives a fitness function (FF) involving seven distinct parameters connected to WSN. To accomplish security, trust factor and link quality metrics are considered in the FF. The design of RO algorithm for secure clustering process shows the novelty of the work. In order to demonstrate the enhanced performance of the CROC-SEHWSN approach, a wide range of simulations are carried out and the outcomes are inspected in distinct aspects. The experimental outcome demonstrated the superior performance of the CROC-SEHWSN technique on the recent approaches with maximum network lifetime of 387.40 and 393.30 s under two scenarios.

Sensor Network Structure Recognition Based on P-law

A sensor graph network is a sensor network model organized according to graph network structure. Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks. In sensor networks, network structure recognition is the basis for accurate identification and effective prediction and control of node states. Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks, based on the characteristics of sensor networks, a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node. This method which builds on unit patulousness and core node signal propagation (called p-law) can rapidly and effectively achieve the global structure identification of a sensor graph network. Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering, the proposed method reveals the natural evolution process and law of graph network subgroup generation. Experimental results confirm the effectiveness, accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition.

Blockchain-based Light-weight Authentication Approach for a Multiple Wireless Sensor Network

ABSTRACT The Internet of Things (IoT) uses Wireless Sensor Networks (WSNs) to gather data from particular locations. Environmental monitoring, Army, agronomic, disaster risk management, and tracking systems are just a few of the industries that heavily rely on the WSN of the IoT. Wireless Sensor Network (WSN) is the massive number of sensor nodes dispatched to track a large area, where the control strategies are frequently challenging. In order to prepare these networks for the market, sufficient protection requirements for medium- and large-scale WSNs are a challenging and important target to achieve. To ensure its security, node identity authentication is a significant one. So, we proposed a public blockchain-based multi-wireless sensor network verification method through a Light-Weight Authentication Algorithm (LWAA) for IoT which is used to enhance the secure authentication and performance in WSN in a multi-Wireless Sensor Network model. Here, the nodes are separated between access points, group head nodes, and regular nodes indicated by their power variations that are created into a hierarchical model. The common authentication of nodes in a number of communication scenarios is identified through blockchain. Our cryptography technique enhances the network life span and decreases the computation time efficiently. The evaluated performance in total power consumption, throughput, latency, packet delivery ratio, time computation, and also proved better results in comparison with the existing authentication methods such as ECDSA, EIBAS, ECBR, and AES.

A New 2D Displacement Measurement Method Based on an Eddy Current Sensor and Absolute Encoding

A new method of two-dimensional (2D) plane displacement measurement based on an eddy current sensor is proposed in this paper. A series of grooves with different widths and depths are graved on the linear displacement table to form 2D absolute coding using the idea of pseudorandom coding. The eddy current sensor array is arranged above the groove to identify the coding. An artificial neural network is used to establish a measurement model which is the mapping relationship between the output of the eddy current sensor array and the 2D displacement of the workbench. A feasibility experiment showed that in the range of 20 × 20 mm, the root mean square error of measurement in the X- and Y-directions are 83 and 73 μm, respectively. The new method integrates eddy current sensor and artificial neural network modeling to realize 2D displacement measurement, which provides a new solution for displacement and angle measurement.

Energy enhancement of routing protocol with hidden Markov model in wireless sensor networks

Energy enhancement of routing protocol with hidden Markov model in wireless sensor networks