Wireless Sensor Network Model Research Articles

Mobility target tracking with meta‐heuristic aided target movement prediction scheme in WSN using adaptive distributed extended Kalman filtering

SummaryIn wireless sensor networks (WSNs), target tracking has been prominently raised in recent days. Because of the frequent utilization of WSN, the attention on target tracking is greatly increased. The estimated optimal value derived from the earlier moment is rarely taken into consideration in traditional target‐tracking algorithms. One of the most crucial uses of WSNs is mobile target tracking, and it is especially used for spying. Precision surveillance is heavily dependent on localization or distance estimation, and extensive study has been done in this area. This research aims to develop a new target‐tracking network‐assisted target movement prediction model in WSN with reduced energy consumption. The major phases involved in the proposed target movement prediction in WSN are (a) mobility target tracking and (b) target movement prediction. Initially, the mobility target tracking is done with the help of adaptive distributed extended Kalman filtering (ADEKF). The mobility target tracking performance is improved by optimally tuning the parameters from ADEKF with the support of the improved squid game optimizer (ISGO). Then, the target movement prediction phase is executed with the help of input parameters like “Angle of Arrival (AoA) and Received Signal Strength (RSS),” and the optimal progress of the mobile node is predicted. The implementation outcome of the proposed tracking network‐assisted target movement prediction model is validated concerning various performance metrics. Overall performance analysis shows that the developed model offers 2.5% in terms of RMSE measures. The developed model shows better analysis while validating with existing approaches.

The Modeling and Detection of Attacks in Role-Based Self-Organized Decentralized Wireless Sensor Networks

This article discusses the modeling and detection of attacks in self-organizing decentralized wireless sensor networks (WSNs) that can be applied to various critical scenarios in practice. Security issues in this type of network have previously been studied to a rather poor extent. In particular, existing attack detection approaches and algorithms do not rely on the properties of self-organization and decentralization, which an attacker is able to exploit to compromise the network and its services. We propose, first, a model of a self-organizing decentralized wireless sensor network; second, a model of the attacks on such networks; third, algorithms for data collection and attack detection; and, finally, a technique for their application. The WSN model represents a formal specification of this type of network, defining the conditions and limitations of network self-organization and decentralization. The model is characterized by a proposed underlying role-based operation of network nodes and a set of their functional states. The proposed attack model covers the possible types of attacks that are relevant to a given type of WSN and are based on the exploitation of the self-organization and decentralization of the network. The developed algorithm for collecting data for attack detection presents specific types of data and their sources. The developed combined attack detection algorithm is formed of actions that detect relevant attacks on self-organizing decentralized WSNs using machine learning methods. The distinctive element of this algorithm is a set of highly specific features that are obtained by analyzing the data collected in the WSN and used to detect attacks. The proposed technique combines the constructed models and algorithms for the sake of tuning and deploying the attack detection module and the effective detection of attacks in practice. This technique specifies the main steps for the joint use of the models and algorithms and the assignment of data collection and detection parameters. The results of the experiments confirm the correctness of the constructed models, algorithms and technique due to the high values of the attack detection quality indicators. Therefore, the practical application of the proposed apparatus will facilitate improvements in the security of self-organizing decentralized WSNs. Experimental research has confirmed the practical applicability of our proposed solutions. In particular, it has shown that the proposed algorithms and the detection technique can detect both attacks implemented through the exploitation of the network’s properties of decentralization/self-organization and common variations in these attacks (i.e., without exploiting the decentralization property). In general, the experimental results expose a high quality of detection, with an f1-score equal to 0.99.

A fuzzy logic and cross-layered optimization for effective congestion control in wireless sensor networks to improve efficiency and performance

Wireless Sensor Networks (WSNs) are a fundamental component of the Internet of Things (IoT), used in diverse applications to detect environmental conditions and send information to the Internet. WSNs are susceptible to congestion issues, leading to increased packet loss, extended delays, and reduced throughput. This research introduces a Fuzzy Logic-based Cross-Layered Optimization Model (FL-CLOM) for WSNs to tackle the problem. FL-CLOM is developed by including the signal-to-noise ratio of the wireless channels in the Transmission Control Protocol (TCP) approach, bridging the transmission layer and Media Access Control (MAC) layer. A fuzzy logic system is created by integrating fuzzy control with congestion control to dynamically manage the queue size in crowded nodes and minimize the effects of external uncertainties. Various simulations were conducted using MATLAB and NS-2.34 to compare the suggested FL-CLOM to conventional methods. The results indicate that FL-CLOM efficiently adjusts to queue size changes and demonstrates rapid convergence, reduced average delay, reduced packet loss, and increased throughput.

Blockchain Based Wireless Sensor Networks for Detecting Nodes

Abstract The versatility of Wireless Sensor Networks (WSNs) and other unique qualities inspired by real-time collaboration between the sensor nodes make them widely used for a range of applications in monitoring and surveillance. As solutions for malicious node identification involve a single, centralized decision-making process, security in WSNs is becoming more and more important. Errors are difficult to prevent with this paradigm, and repeatability and traceability are problems. Therefore, the detecting ability and integrity of the developed detection method cannot be guaranteed by malicious node identification technology in ordinary WSNs. This article provides insights into this innovative idea as well as a thorough investigation of the use of blockchain technology to WSNs (Blockchain-Based WSN), including a full analysis of a blockchain-based method for identifying rogue nodes. The Blockchain-Based WSN architecture, business-specific applications, and use are all included in this study. The Blockchain-Based WSN architecture model for tracking malicious nodes and the smart contract features of malicious node detection is both included in the explanation of harmful node identification based on Blockchain-Based WSN in this paper. The second part of this study investigates the use of blockchain technology to the management of WSN data, which necessitates online information aggregation and may also require auditing, event logging, and storage for information analysis and offline query processing. The traditional WSN data management approaches are discussed first in this study, followed by the blockchain-based WSN data management choices. The benefaction of blockchain to WSN security management is also included in this survey. Before discussing blockchain-based WSN security management solutions, such as those that provide access control, protect data integrity, ensure anonymity, and prolong the life of WSN nodes, it first looks at the security vulnerabilities of centralized WSN models.

A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks

Many real-time applications make use of advanced wireless sensor networks (WSNs). Because of the limited memory, power limits, narrow communication bandwidth, and low processing units of wireless sensor nodes (SNs), WSNs suffer severe resource constraints. Data prediction algorithms in WSNs have become crucial for reducing redundant data transmission and extending the network's longevity. Redundancy can be decreased using proper machine learning (ML) techniques while the data aggregation process operates. Researchers persist in searching for effective modelling strategies and algorithms to help generate efficient and acceptable data aggregation methodologies from preexisting WSN models. This work proposes an energy-efficient Adaptive Seagull Optimization Algorithm (ASOA) protocol for selecting the best cluster head (CH). An extreme learning machine (ELM) is employed to select the data corresponding to each node as a way to generate a tree to cluster sensor data. The Dual Graph Convolutional Network (DGCN) is an analytical method that predicts future trends using time series data. Data clustering and aggregation are employed for each cluster head to efficiently perform sample data prediction across WSNs, primarily to minimize the processing overhead caused by the prediction algorithm. Simulation findings suggest that the presented method is practical and efficient regarding reliability, data reduction, and power usage. The results demonstrate that the suggested data collection approach surpasses the existing Least Mean Square (LMS), Periodic Data Prediction Algorithm (P-PDA), and Combined Data Prediction Model (CDPM) methods significantly. The proposed DGCN method has a transmission suppression rate of 92.68%, a difference of 22.33%, 16.69%, and 12.54% compared to the current methods (i.e., LMS, P-PDA, and CDPM).

A hybrid sensing of rotation-induced stress of segmental lining during shield tunneling via WSN and surrogate numerical modeling

The structural responses of segmental lining to the construction loads during shield tunneling are not well understood yet. To this end, a hybrid sensing method is proposed to understand the mechanical responses of tunnel lining to the construction loads via a wireless sensor network (WSN) and machine learning algorithms. Firstly, the displacement of tunnel lining is monitored in real-time during construction by using the WSN. The rotation of the whole segmental ring is found for the first time based on the in-situ data. A refined three-dimensional numerical model is then applied to investigate the additional stress of segmental rings under such a new deformation mode. Subsequently, a scenario-based construction database is established, including 130 numerical cases of load conditions. For the dynamic control of shield tunneling, an efficient multi-layer perception surrogate model is proposed with the help of the established database. The additional stress state of segmental rings is sensed on-site in a hybrid mode efficiently, both from real structural responses via WSN and the construction parameters via surrogate model. Meanwhile, the construction parameters can be adjusted timely if the additional stress exceeds its limit. For the case study of this paper, with the measured maximum ring rotation at 0.25° and the maximum arc length of circumferential misalignment between two adjacent rings at 7.1 mm, an additional rotation-induced stress of 19.34*103 kN/m2, i.e., 39% of the concrete compressive strength, is found for the first time at longitudinal bolt holes of segmental rings, which has not been considered but should be emphasized in the future design practice.

On Wireless Sensor Network Models: A Cross-Layer Systematic Review

Wireless sensor networks (WSNs) have been adopted in many fields of application, such as industrial, civil, smart cities, health, and the surveillance domain, to name a few. Fateway and sensor nodes conform to WSN, and each node integrates processor, communication, sensor, and power supply modules, sending and receiving information of a covered area across a propagation medium. Given the increasing complexity of a WSN system, and in an effort to understand, comprehend and analyze an entire WSN, different metrics are used to characterize the performance of the network. To reduce the complexity of the WSN architecture, different approaches and techniques are implemented to capture (model) the properties and behavior of particular aspects of the system. Based on these WSN models, many research works propose solutions to the problem of abstracting and exporting network functionalities and capabilities to the final user. Modeling an entire WSN is a difficult task for researchers since they must consider all of the constraints that affect network metrics, devices and system administration, holistically, and the models developed in different research works are currently focused only on a specific network layer (physical, link, or transport layer), making the estimation of the WSN behavior a very difficult task. In this context, we present a systematic and comprehensive review focused on identifying the existing WSN models, classified into three main areas (node, network, and system-level) and their corresponding challenges. This review summarizes and analyzes the available literature, which allows for the general understanding of WSN modeling in a holistic view, using a proposed taxonomy and consolidating the research trends and open challenges in the area.

Cascading Robustness Analysis of Wireless Sensor Networks with Varying Multisink Placement.

In practical wireless sensor networks (WSNs), cascading failures are closely related to network load distribution, which in turn strongly relies on the locations of multiple sink nodes. For such a network, understanding how the multisink placement affects its cascading robustness is essential but still largely missing in the field of complex networks. To this end, this paper puts forward an actual cascading model for WSNs based on the multisink-oriented load distribution characteristics, in which two load redistribution mechanisms (i.e., global routing and local routing) are designed to imitate the most commonly used routing schemes. On this basis, a number of topological parameters are considered to quantify the sinks' locations, and then, the relationship between these quantities with network robustness is investigated on two typical WSN topologies. Moreover, by employing the simulated annealing approach, we find the optimal multisink placement for maximizing network robustness and compare the topological quantities before and after the optimization to validate our findings. The results indicate that for the sake of enhancing the cascading robustness of a WSN, it is better to place its sinks as hubs and decentralize these sinks, which is independent of network structure and routing scheme.

Certificateless Public Auditing Scheme With Data Privacy and Dynamics in Group User Model of Cloud-Assisted Medical WSNs.

With the application of wireless sensor network (WSN) in healthcare field, online sharing of medical data has attracted more and more attention. However, wearable sensor nodes are limited in energy, storage space and data processing capacity, which largely restricts their deployment in resource demand application scenarios. Fortunately, cloud storage services can enrich the capabilities of wearable sensors and provide an effective method for people to share data within a group. However, as medical data directly relates to patients' health and privacy information, ensuring the integrity and privacy of medical records stored in cloud servers becomes a key issue to be urgently solved. Many public data auditing schemes have been put forward to address the above issues. Unfortunately, most of them have security vulnerabilities or poor functionality and performance. In this paper, we come up with a secure and efficient certificateless public auditing scheme for cloud-assisted medical WSNs, which not only supports dynamic data sharingand privacy protection, but also achieves efficient group user revocation. Security analysis and performance evaluation demonstrate that our scheme significantly reduce the total computation cost while achieving a higher security level. Compared with other related schemes, our new proposal is more suitable for group user data sharing in cloud-assisted medical WSNs.

Application of improved black hole algorithm in prolonging the lifetime of wireless sensor network

Sensor technology is developing rapidly and up to date. The lifetime of the Wireless Sensor Network (WSN) has also attracted many researchers, and the location of the Base Station (BS) plays a crucial role in prolonging the lifetime. The energy consumption in the WSN occurs during transmission of data and selection of cluster-head nodes. A reasonable location of the BS prolongs the lifetime of the WSN. This study proposes a Levy Flight Edge Regeneration Black Hole algorithm (LEBH) to speed up convergence and enhance optimization capabilities. The performance of LEBH and other heuristic algorithms are compared on CEC 2013. The result shows that the LEBH outperforms other heuristics in most cases. In this study, the energy consumption and WSN models are simulated. Subsequently, the proposed LEBH is combined with relocation technology to change the location of the BS to prolong the lifetime. Simulation results show LEBH-BS prolongs the lifetime of the WSN better than random-base and static-base stations and other heuristic algorithms in most cases.

Deep Reinforcement Learning-Based UAV Path Planning for Energy-Efficient Multitier Cooperative Computing in Wireless Sensor Networks

Benefiting from the progress of microelectromechanical system (MEMS) technology, wireless sensor networks (WSNs) can run a large number of complex applications. One of the most critical challenges for complex WSN applications is the huge computing demands and limited battery energy without any replenishment. The recent development of UAV-assisted cooperative computing technology provides a promising solution to overcome these shortcomings. This paper addresses a three-tier WSN model for UAV-assisted cooperative computing, which includes several sensor nodes, a moving UAV equipped with computing resources, and a sink node (SN). Computation tasks arrive randomly at each sensor node, and the UAV moves around above the sensor nodes and provides computing services. The sensor nodes can process the computation tasks locally or cooperate with the UAV or SN for computing. In a life cycle of the UAV, we aim to maximize the energy efficiency of cooperative computing by optimizing the UAV path planning on the constraints of node energy consumption and task deadline. To adapt to the time-varying indeterminate environment, a deep Q network- (DQN-) based path planning algorithm is proposed. Simulation studies show that the performance of the proposed algorithm is better than the competitive algorithms, significantly improves the energy efficiency of cooperative computing, and achieves energy consumption balance.

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.

Signal-Power-Based Combination Weight Rule for Distributed Blind Equalization in WSN Model

In this paper, we consider how to improve the performance of distributed estimation over a diffusion network in order to estimate the unknown parameter of interest from noisy measurements. Specifically, the distributed blind equalization based on the single-input multiple-output channel model over a wireless sensor network (WSN) is discussed in this paper. We propose a new combination weight rule that the weight of each sensor node is assigned by the signal power of the sensor (received) signal instead of only the degree of each sensor node. We assume that each channel from the common transmitter to the sensor nodes in the WSN is common and that noises, the variances of which are different, are included at each channel. The average mean square error and symbol error rate performance characteristics of the distributed blind equalization are investigated. Simulations show that a significant performance improvement is obtained by employing the proposed combination weight rule compared with using conventional combination weight rules.

Integration of Block chain Model for Energy Efficient WSN for IOT Application

Abstract: Wireless Sensor Networks (WSNs) play a key role in the Internet of Sensor Things (IoST).IoST helps collect data from environments and is used in energy trading, monitoring, smart grids, and more. Connect to the Internet and automate your surveillance system without third-party involvement. An IoST network consists of Sensor nodes that perform environmental monitoring. Wireless Sensor Networks (WSN) and the Internet of Things (I.o.T) have gained popularity in recent years as the underlying infrastructure for connected devices and sensors in a variety of sectors. The data generated by these sensors are used in smart cities, agriculture, transportation systems, healthcare systems, toll collection systems, automatic identification of road data, automatic identification of vehicle license plates, and more. It has become a proposed blockchain mechanism. The main problems and challenges of WSN are effectively reduced by using the LEACH protocol with efficient cluster head selection.

Adaptive Route Sink Relocation Using Cluster Head Chain Cycling Model in WSN

Wireless Sensor Networks (WSN) have revolutionized the processes involved in industrial communication. However, the most important challenge faced by WSN sensors is the presence of limited energy. Multiple research investigations have been conducted so far on how to prolong the energy in WSN. This phenomenon is a result of inability of the network to have battery powered-sensor terminal. Energy-efficient routing on packet flow is a parallel phenomenon to delay nature, whereas the primary energy gets wasted as a result of WSN holes. Energy holes are present in the vicinity of sink and it is an important efficient-routing protocol for WSNs. In order to solve the issues discussed above, an energy-efficient routing protocol is proposed in this study named as Adaptive Route Decision Sink Relocation Protocol using Cluster Head Chain Cycling approach (ARDSR-CHC2H). The proposed method aims at improved communication at sink-inviting routes. At this point, Cluster Head Node (CHN) is selected, since it consumes low energy and permits one node to communicate with others in two groups. The main purpose of the proposed model is to reduce energy consumption and define new interchange technology. A comparison of simulation results demonstrates that the proposed algorithm achieved low cluster creation time, better network error and high Packet Delivery Rate with less network failure.

Wireless Sensor Network Optimization Using Genetic Algorithm

Wireless Sensor Network (WSN) is a high potential technology used in many fields (agriculture, earth, environmental monitoring, resources union, health, security, military, and transport, IoT technology). The band width of each cluster head is specific, thus, the number of sensors connected to each cluster head is restricted to a maximum limit and exceeding it will weaken the connection service between each sensor and its corresponding cluster head. This will achieve the research objective which refers to reaching the state where the proposed system energy is stable and not consuming further more cost. The main challenge is how to distribute the cluster heads regularly on a specified area, that’s why a solution was supposed in this research implies finding the best distribution of the cluster heads using a genetic algorithm. Where using an optimization algorithm, keeping in mind the cluster heads positions restrictions, is an important scientific contribution in the research field of interest. The novel idea in this paper is the crossover of two-dimensional integer encoded individuals that replacing an opposite region in the parents to produce the children of new generation. The mutation occurs with probability of 0.001, it changes the type of 0.05 sensors found in handled individual. After producing more than 1000 generations, the achieved results showed lower value of fitness function with stable behavior. This indicates the correct path of computations and the accuracy of the obtained results. The genetic algorithm operated well and directed the process towards improving the genes to be the best possible at the last generation. The behavior of the objective function started to be regular gradually throughout the produced generations until reaching the best product in the last generation where it is shown that all the sensors are connected to the nearest cluster head. As a conclusion, the genetic algorithm developed the sensors’ distribution in the WSN model, which confirms the validity of applying of genetic algorithms and the accuracy of the results.

An Efficient Authenticated Elliptic Curve Cryptography Scheme for Multicore Wireless Sensor Networks

The need to ensure the longevity of Wireless Sensor Networks (WSNs) and secure their communication has spurred various researchers to come up with various WSN models. Prime among the methods for extending the life span of WSNs is the clustering of Wireless Sensors (WS), which reduces the workload of WS and thereby reduces its power consumption. However, a drastic reduction in the power consumption of the sensors when multicore sensors are used in combination with sensors clustering has not been well explored. Therefore, this work proposes a WSN model that employs clustering of multicore WS. The existing Elliptic Curve Cryptographic (ECC) algorithm is optimized for parallel execution of the encryption/decryption processes and security against primitive attacks. The Elliptic Curve Diffie-Helman (ECDH) was used for the key exchange algorithm, and the Elliptic Curve Digital Signature Algorithm (ECDSA) was used to authenticate the communicating nodes. Security analysis of the model and comparative performance analysis with the existing ones were demonstrated. The security analysis results reveal that the proposed model meets the security requirements and resists various security attacks. Additionally, the projected model is scalable, energy-conservative, and supports data freshness. The results of comparative performance analysis show that the proposed WSN model can efficiently leverage multiprocessors and/or many cores for quicker execution and conserves power usage.

Neural networking study of worms in a wireless sensor model in the sense of fractal fractional

<abstract><p>We are concerned with the analysis of the neural networks of worms in wireless sensor networks (WSN). The concerned process is considered in the form of a mathematical system in the context of fractal fractional differential operators. In addition, the Banach contraction technique is utilized to achieve the existence and unique outcomes of the given model. Further, the stability of the proposed model is analyzed through functional analysis and the Ulam-Hyers (UH) stability technique. In the last, a numerical scheme is established to check the dynamical behavior of the fractional fractal order WSN model.</p></abstract>

Design of WSN Model with NS2 for Animal Tracking and Monitoring

Tracking and monitoring wild life is a significant ecological issue that cannot be overlooked. Especially getting fundamental indications of wild animal like Tigers is critical and evaluating their wellbeing without harming their habitat is another important task and recording their vital signs by sedating causes stress and injuries in animal. This paper proposes a design of wireless based tracking and health monitoring system for Tigers using WSN and Network simulator 2. The important physiological parameters like temperature of heart beat rate, sound signal, respiration rate, accelerometer signal and GPS data is transmitted and monitored through wireless sensor network (WSN).A sensor network is composed of different sensors which remotely communicate with each other and the information of each node are separated. Each node has ability to communicate among each other or to a specific node to communicate with sink node A Network simulator which acts as simulation tool for network of sensor nodes in tracking and monitoring system is employed to simulate the location and health of tigers. This system empowers veterinarians and forest guards to precisely and accurately take an animal's vital signs and analyses the health and location. We analyze the performance of simulation model by using metrics, such as average throughput of all nodes, end to end delay, packet received, packet dropped and packet delivery ratio (PDR), average good put and energy model.

Data transmission reduction using prediction and aggregation techniques in IoT-based wireless sensor networks

Currently, broad ranges of economic sectors exploit the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) technology generating problems to be managing, processing and organizing the big data streams. Given the inherent sensor constraints (battery-dependent life, lack of memory space, limited processor power), there is a need for systems capable of reducing data transmission flux of the distributed IoT-based WSNs applications to mitigate the continuously increasing network traffic. Prediction and data aggregation techniques are promising solutions to meet this requirement. These techniques are particularly powerful solutions in forecasting processes where a huge data to be collected, transmitted and recorded. This is right even if we consider the integration of IoT-based WSNs with cloud computing technology. This paper proposes the combination of the recently developed prediction scheme EADPS (Extended Adaptive Dual Prediction Scheme) and the temporal correlation based data aggregation technique as a power tool to address the problem of communication reduction in connected IoT-based WSNs. First, we studied separately the impact of each technique before considering their combination. To achieve this goal, we consider the multi-hop ring model of the IoT-based WSNs. The results show that the aggregation technique is powerful compared to the DPS prediction scheme but loses its superiority for small WSNs size (with lesser than five rings) when the EADPS schema is applied. In addition, the data correlation has a feeble impact on reduction of transmission rates. According to the imposed tolerance thresholds, we show that the proposed scheme reduced the average transmission rates in the range 85%–96% for the entire network. The obtained results are presented and discussed.