Location Of Sensor Nodes Research Articles

Network Architecture for Intelligent Identification of Faults in Rabbit Farm Environment Monitoring Based on a Biological Neural Network Model.

Currently, livestock and poultry farming is gradually developing towards modernization and scale, and closed livestock and poultry farms are widely used for poultry feeding management, but at the same time, the farming risks of large-scale farms are increasing. In this paper, based on the study of wireless sensor networks and biological neural network models, the environmental factors that mainly affect the growth of domestic rabbits are analyzed as an example, and the technology is used to design and implement an environmental monitoring system for modern farms. The design of the system is divided into three main parts: hardware design of each node, software design, and upper computer monitoring software design. The hardware part of the system uses coordinator nodes, router nodes, sensor nodes, and control nodes to form a wireless sensor network in the farm, carries out the hardware circuit design of each node, and based on the protocol stack, designs the software program of each node to realize the collection, transmission, and regulation of environmental information in the farm. In the upper computer part, the design and development of the upper computer monitoring software interface are used to complete the real-time display of environmental data, historical query, database storage, and curve drawing, and to design a remote client data query system based on the architecture to realize the query of environmental data of the farm by remote users and to carry out monitoring fault intelligent identification alarm. At the same time, the paper investigates the optimal deployment of wireless sensor network nodes and searches for the optimal location of sensor nodes through an improved biological neural network algorithm to maximize the network coverage and reduce the coverage of blind areas, and conducts simulation experiments with the coverage rate of a rabbit farm as the optimization target.

A Robust, Scalable, and Energy-Efficient Routing Strategy for UWSN Using a Novel Vector-Based Forwarding Routing Protocol

The Underwater Wireless Sensor Network (UWSN) is capable of supporting a wide range of low-data-rate acoustic sensor networks, as well as scalability and energy-efficient routing algorithms. Furthermore, because of the lower bandwidth and longer propagation delays, energy consumption is a major concern with underwater networks. Because radio transmissions are unstable in deep water, a UWSN often communicates via acoustic channels. UWSN features create constraints on data packet transmission and energy-efficient routing. The hardest challenge is creating an effective routing protocol for UWSNs that uses sensor node localization. There have been several routing algorithms reported for identifying nodes via a localization process. This paper proposes a novel vector-based forwarding (NVBF) and efficient depth-based routing (DBR) protocol to provide robust, scalable, and energy-efficient routing. Efficient DBR is a stochastic model that can survive even in acoustic channels with substantial transmission loss. An adjacent node table is created to decrease the energy consumption and end-to-end delay by limiting the adjacent node request generated every single time. The redundant packet transmission is reduced by creating a packet queue which also minimizes the energy. The NVBF algorithm uses a random waiting time to overcome the collision that occurs while sending the acknowledgment packets. Based on the previous sender’s depth and the depth of the own node, the DBR makes packet transmission decisions. The experiments are conducted using the NS3 simulator and the efficiency of the proposed approach is evaluated in terms of propagation delay, network lifetime, energy consumption, and delivery ratio. When compared to the existing techniques such as DBR, VBF, and energy-efficient DBR (EDBR), the proposed methodology offers improvement up to 40% and 30% in terms of propagation delay and delivery ratio.

Machine Learning-Based Prediction of Node Localization Accuracy in IIoT-Based MI-UWSNs and Design of a TD Coil for Omnidirectional Communication

This study aims to realize Sustainable Development Goals (SDGs), i.e., SDG 9: Industry Innovation and Infrastructure and SDG 14: Life below Water, through the improvement of localization estimation accuracy in magneto-inductive underwater wireless sensor networks (MI-UWSNs). The accurate localization of sensor nodes in MI communication can effectively be utilized for industrial IoT applications, e.g., underwater gas and oil pipeline monitoring, and in other important underwater IoT applications, e.g., smart monitoring of sea animals, etc. The most-feasible technology for medium- and short-range communication in IIoT-based UWSNs is MI communication. To improve underwater communication, this paper presents a machine learning-based prediction of localization estimation accuracy of randomly deployed sensor Rx nodes through anchor Tx nodes in the MI-UWSNs. For the training of ML models, extensive simulations have been performed to create two separate datasets for the two configurations of excitation current provided to the Tri-directional (TD) coils, i.e., configuration1-case1_configuration2-case1 (c1c1_c2c1) and configuration1-case2_configuration2-case2 (c1c2_c2c2). Two ML models have been created for each case. The accuracies of both models lie between 95% and 97%. The prediction results have been validated by both the test dataset and verified simulation results. The other important contribution of this paper is the development of a novel assembling technique of a MI-TD coil to achieve an approximate omnidirectional magnetic flux around the communicating coils, which, in turn, will improve the localization accuracy of the Rx nodes in IIoT-based MI-UWSNs.

An evolved algorithm for underwater acoustic sensor node localization enhancement using reference node

Accurate localization of underwater acoustic sensor networks is one of the essential operations. The range-free DV-Hop technique is more acceptable due to its low hardware cost and simplicity than the range-based approach. But the main drawback of this basic DV-Hop method is low accuracy and link failure due to maintenance or short battery life during the continuous localization calculation in the underwater scenario. Most algorithms in this field do not pay enough attention to the mobility of the nodes. By examining the movement patterns of water, this manuscript uses a technique for UWSN localization based on a mobility prediction algorithm. This manuscript proposed a reference node-based multi-hop improved DV-Hop​ technique using School Topper Optimization (STO) and mobility prediction algorithms. This proposed scheme’s highlighted advantage is to reduce the localization error to identify the unknown target nodes using a reference node-based localization scheme using a mobility prediction algorithm without increasing the beacon nodes, and the multi-hop improved DV-Hop​ algorithm using STO significantly improved the link failure problem by iteratively selecting the different sets of the beacon nodes in the same scenario The simulation report has proved that our framework takes better computational time, has a better convergence rate by introducing the STO technique, provides 23.76% and 37.18% better avg. localization error and localization error variance, and 16.12% better coverage from PSODV-Hop and GSODV-Hop techniques.

Modified Zeroing Neurodynamics Models for Range-Based WSN Localization From AOA and TDOA Measurements

With the rise of the internet of things, wireless sensor network (WSN) technology has gained unprecedented development and has attracted increasing attention from researchers. Due to the inherent characteristics of WSN, such as interaction with the environment, WSN localization becomes an essential and attractive topic in academia and industry. In this paper, the range-based localization problem in a mobile WSN application scenario is considered to be time-varying and modeled as a dynamic matrix equation by introducing the time parameter. Two modified zeroing neurodynamics (ZND) models are proposed and investigated to deal with range-based WSN localization problems from angle of arrival (AOA) measurement and time difference of arrival (TDOA) measurement. In addition, the convergence of the proposed models is theoretically analyzed. Furthermore, computer simulations on WSN localization are carried out to prove the effectiveness of the proposed models in terms of accuracy and robustness to the dynamic environment. Additionally, the application to underwater sensor node localization of underwater acoustic network (UAN) testbed is provided to illustrate the feasibilities of the proposed models for solving UAN localization problem.

Hybrid localization scheme using K‐fold optimization with machine learning in WSN

SummaryNode localization technology can identify and track nodes, making observing data more relevant; for example, information received at the sink node would be useless to the client if node localization data from the sensor region were not included. Localization is described as determining the location of unknown sensor nodes named destination nodes applying the recognized location of anchor nodes based on measurements such as time difference of occurrence, time of occurrence, angle of occurrence, triangulation, and maximum probability. The purpose of node localization is to assign coordinates points to all sensor nodes arbitrarily put in the monitoring region and have an unknown location. Localization of nodes is essential to account for the cause of events that help group sensor querying, routing, and network coverage. In this paper, data transmission among the nodes is done by comparing the received signal strength indicator (RSSI) value with the supervised learning value. If the RSSI value is less than the supervised learning value, the data transmission takes place; else, no transmission. This paper proposes a hybrid localization scheme that effectively uses K‐fold optimization with supervised learning and gives good results for distance error and RSSI/energy efficiency. The proposed scheme can effectively detect the optimal path for data transmission, node localization for the destination, and overall performance enhancement using threshold decision making.

A Novel Unmanned Aerial Vehicle Charging Scheme for Wireless Rechargeable Sensor Networks in an Urban Bus System

Wireless sensor networks (WSNs) are implemented in many aspects of daily life, such as Internet of Things applications, industrial automation, and intelligent agriculture. Sensors are typically powered by batteries. Chargers can be used to supply power to sensor nodes and thus extend the lifetime of WSNs. This special type of network is named a wireless rechargeable sensor network (WRSN). However, due to the limited battery power and different deployment locations of the sensors, efficiently moving the chargers from the current sensor nodes to the next sensor nodes is a challenge. In this study, we propose an unmanned aerial vehicle (UAV)-based charging scheme in an urban bus system, involving the coordination between UAVs and bus schedules. The UAVs can be recharged by urban buses and then supply the power to sensor nodes. We implemented three charging strategies: naïve, shortest path, and max power. In the naïve strategy, the UAVs fly directly to sensor nodes when the sensors are lacking power. In the shortest path strategy, the minimum distance between the sensor node and bus location is calculated, and the UAVs fly the shortest path to the sensor nodes. In the maximum power charging strategy, the UAV that has the highest battery power is assigned to work. The experimental results show that the shortest path charging and max power charging strategies perform better than naïve charging in different parameter settings. To prolong the lifetime of the network system, adjusting the bus frequency according to the number of nearby sensors around the bus route is favorable.

A novel hybrid range-free approach to locate sensor nodes in 3D WSN using GWO-FA algorithm

The precise node location of the sensor nodes is an essential requirement in wireless sensor networks (WSNs) to determine the place or event occurring at a particular instant of time. In WSN, existing localization schemes consider two-dimensional (2D) space, while in actual life, sensor nodes are placed in three-dimensional (3D) space. In 3D localization, there are many research challenges, such as higher computational complexity, poor location prediction, lesser coverage, and depending only on fewer anchor nodes. To address various research issues in a 3D environment we propose a range-free technique applied in an anisotropic scenario having degree of irregularity (DOI) as 0.01 using the concepts of a fuzzy logic system (FLS). Anisotropic properties of nodes are considered to determine the efficiency of Grey wolf with the Firefly algorithm. In our proposed scenario, the received signal strength (RSS) information is necessary among the target nodes and their corresponding anchor nodes for determining the location of target nodes using the information based on edge weights. These edge weights are further modeled using Hybrid Grey Wolf Optimization with Firefly Algorithm (GWO-FA) to estimate the location of target nodes. The proposed algorithm is energy efficient as a single location-aware node is used for localization. Further, the concept of virtual anchors is introduced that helps the algorithm to determine 3D positions.

A new and reliable intelligent model for deployment of sensor nodes for IoT applications

To ensure reliable Internet of Things (IoT) applications, this study provides a new intelligent deployment technique for sensor nodes. The proposed intelligent deployment technique places sensor nodes in appropriate locations to achieve high levels of connectivity and, as a result, boost the WSN’s overall reliability. To generate optimal sensor node coordinate positions, a modified version of the Expectation–Maximization method is utilized as a machine learning technique. Following that, each node’s neighbours are determined, and links between them are built using the K-Nearest neighbour algorithm. Then, to assess the reliability of WSN, a novel algorithm is proposed. The proposed algorithms are all well-illustrated with appropriate examples. When the algorithms provided in this research are compared to certain existing methods in terms of node positioning accuracy (10%), network connectivity (10%), and estimated dependability values (5%), it is clear that the suggested strategy outperforms them in every way.

Deterministic Static Sensor Node Placement in Wireless Sensor Network based on Territorial Predator Scent Marking Behaviour

An optimum sensor node placement mechanism for Wireless Sensor Network (WSN) is desirable in ensuring the location of sensor nodes offers maximum coverage and connectivity with minimum energy consumption. This paper proposes a sensor node placement algorithm that utilizes a new biologically inspired optimization algorithm that imitates the behaviour of a territorial predator in marking their territories with their odours known as Territorial Predator Scent Marking Algorithm (TPSMA). The main objectives considered in this paper are to achieve maximum coverage and minimum energy consumption with guaranteed connectivity. A simulation study has been carried out to compare the performance of the proposed algorithm implemented in two different single objective approaches with an Integer Linear Programming based algorithm. The proposed single objective approaches of TPSMA studied in this paper are TPSMA with minimum energy and TPSMA with maximum coverage. Simulation results show that the WSN deployed using the proposed TPSMA sensor node placement algorithm is able to arrange the sensor nodes according to the objective required; TPSMA with maximum coverage offers the highest coverage ratio with fewer sensor nodes up to 100% coverage while TPSMA with minimum energy consumption utilized the lowest energy as low as around 4.85 Joules. Full connectivity is provisioned for all TPSMA approaches since the constraint of the optimization problem is to ensure the connectivity from all sensor nodes to the sink node.

Localization in Mobile Sensor Networks using Kalman Filter

Objectives: The objectives of this study is to propose a localization scheme that meets the requirements of Mobile Sensor Networks (MSNs) in terms of high localization accuracy, lower power consumption and high network coverage. Methods: In this study, a localization scheme has been proposed to allow sensor nodes to estimate their locations using information transmitted by a set of anchor nodes that know their own locations. The proposed scheme performs localization process in a more general network environment where no special hardware for ranging is available, the prior deployment of anchor nodes is unknown, the anchor nodes density is low, the node distribution is irregular, and where sensor nodes and anchors can move uncontrollably. The proposed scheme applies the Kalman Filter (KF) technique to combine the information from anchor nodes to estimate the location of sensor nodes. The KF takes into account the different uncertainties and error sources that disturb the localization process. Findings: The proposed scheme has been compared with the two classes of Flexible Optimal Kalman Filtering FOKF algorithm (FOKFO and FOKF-R) under different mobility models and extensive simulations in large scale network. The results show that the proposed scheme has a lower localization error and lower communication cost than the FOKF algorithm which leads to maximizing network lifetime and energy saving. The proposed scheme has an improved percentage of localization accuracy than FOKF-O and FOKF-R by 5.0% and 4.0% respectively, also it has an improved percentage of coverage than FOKF-O and FOKF-R by 10.0% and 5.0% respectively, finally it has a less number of transmitted beacon messages than FOKF-O and FOKFR by 171 and 113 packets respectively which leads to maximizing network lifetime. Novelty/Applications: The proposed scheme is considered a suitable localization solution for resource-limited sensor nodes in terms of memory limitations, energy consumption and localization accuracy. Keywords: Localization; Mobile Sensor Networks; Kalman Filter; FOKFO; FOKFR

A novel Sugeno type 2 Fuzzy system for finding error positions applying localization in wireless sensor network

In various usages where Wireless Sensor Networks (WSNs) have been utilized, this is vital to identify wherefrom Sensor nodes (SNs) or which position valuable details is attained. The Global Positioning System (GPS) is traditionally utilized to find out position. Nevertheless, GPS schemes are not perfect for various usages because of their extreme power utilization and high charge. As a substitute to GPS, distance and position may be approximated during the practice of more than three SNs with identified positions. Received Signal Strength Indication (RSSI) is the easiest and largely economical method applied to decide distance and position, and is a criterion aspect on each sensor. Howbeit, RSSI might be influenced through sound and ecological difficulties. Due to this, this is not easy to place up a statistical model for RSSI. The range free technique has various approaches to evaluate the location of SNs in a particular area. In our research work, Centroid localization is employed for development and investigation. RSSI is attained by evaluating the distance between every anchor node (AN) and the SN. Evaluating the position of every SN would be carried out with the help of centroid technique. However, the weights remain the chief metrics in the centroid connection; those are the output of the Sugeno Type 2 fuzzy scheme. Actually, the sugeno type 2 fuzzy technique is obtained RSSIs being inputs to depict the outputs; those are weights of every AN to the SN.

Recovery schemes of Hop Count Matrix via topology inference and applications in range-free localization

Hop Count Matrix (HCM) contains rich connectivity information, which is very important for various Internet of Things (IoT) applications, especially for obtaining the locations of sensor nodes. However, some items of HCMs may be missing due to attacks by malicious nodes or unexpected termination of flooding operations. To solve this problem, two methods, called HCMR-AM and HCMR-DT, are proposed to recover the missing items. In HCMR-AM, the collected partial hop counts are employed to construct Adjacency Matrix (AM), and then the constructed AM is used to obtain the complete HCM. In HCMR-DT, the recovery of HCM is transformed into a classification problem, where multi-dimensional features are used for joint prediction to achieve more accurate recovery performance. Extensive experimental results demonstrate that compared to the original SVT and HCMR-NBC, our proposed algorithms have significant improvement in recovery performance and execution efficiency. In addition, the complete HCM is used for node localization, and experimental results show that the HCM recovered by the proposed methods can achieve the same localization performance as the HCM without missing value when the observation ratio of HCM is greater than 30%, which cannot be achieved by other recovery algorithms.

Path planning mechanism for mobile anchor-assisted localization in wireless sensor networks

The detection of a deployed sensor node location plays a vital role in all domains of Wireless Sensor Networks since its location provides the source of the information. Many approaches have been proposed in recent years. One of these approaches uses a single location-aware node, known as the Anchor or Beacon, that exchanges information with other nodes to help in their position estimation. Although this approach provides good accuracy, it creates new challenges in the picture. The key issue is to design an optimal trajectory for the anchor node in order to achieve maximum localization accuracy in the shortest amount of time. In this paper, we propose a static trajectory for a mobile beacon node for localization in WSNs. This trajectory ensures that the localization success ratio can be improved for lower communication range of an anchor node with higher localization precision and minimum localization time as compared to other static models. The proposed trajectory also overcomes the collinearity problem and ensures that all the unknown sensor nodes receive good quality beacon positions for position estimation.

3D Localization for Mobile Node in Wireless Sensor Network

Wireless sensor network (WSN) is an emerging technology that can detect, collect, and transmit information in a specific unknown area in an unknown environment. It is currently playing an increasingly important role in the fields of national defense, medical and health, and daily life. WSN node location information is extremely important in many WSN applications. The data information collected by WSN is developed based on known node location information. The node location is one of the important issues in WSNs. Location information is very important for wireless sensors. A WSN without sensor node location information is meaningless because almost all WSN applications need to know node location information, such as animal populations, tracking research, early warning of building fires, management of goods in warehouses, and traffic monitoring systems. Several research works are underway to expand the 2D positioning algorithm in WSN to 3D regardless of the deployment structure of sensor nodes. This paper proposes an improved Savarese algorithm to the problem of singularity in WSN node localization. The proposed algorithm is a modified version of the conventional Savarese algorithm, and it solves the singularity problem and improved the positioning accuracy. Simulation results show that the proposed algorithm effectively improved system performance, and the accuracy is improved over 2.83% and 2.96% than the existing algorithms. The proposed scheme is effective for indoor environments while it can be deployed outdoor for small-scale.

Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

Localization of sensor nodes in the internet of underwater things (IoUT) is of considerable significance due to its various applications, such as navigation, data tagging, and detection of underwater objects. Therefore, in this paper, we propose a hybrid Bayesian multidimensional scaling (BMDS) based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical, magnetic induction, and acoustic technologies. These communication technologies are already used for communication in the underwater environment; however, lacking localization solutions. Optical and magnetic induction communication achieves higher data rates for short communication. On the contrary, acoustic waves provide a low data rate for long-range underwater communication. The proposed method collectively uses optical, magnetic induction, and acoustic communication-based ranging to estimate the underwater sensor nodes' final locations. Moreover, we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound (H-CRLB). Simulation results provide a complete comparative analysis of the proposed method with the literature.

Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics.

Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have been collected is revealed. Localization of sensor nodes in WSNs plays a significant role in several applications such as detecting the enemy movement in military applications. The major aim of localization problem is to find the coordinates of all target nodes with the help of anchor nodes. In this paper, two variants of bat optimization algorithm (BOA) are proposed to localize the sensor nodes in a more efficient way and to overcome the drawbacks of original BOA, i.e. being trapped in local optimum solution. The exploration and exploitation features of original BOA are modified in the proposed BOA variants 1 and 2 using improved global and local search strategies. To validate the efficiency of the proposed BOA variants 1 and 2, several simulations have been performed for various numbers of target nodes and anchor nodes, and the results are compared with original BOA and other existing optimization algorithms applied to node localization problem. The proposed BOA variants 1 and 2 outperform the other algorithms in terms of mean localization error, number of localized nodes and computing time. Further, the proposed BOA variants 1 and 2 and original BOA are also compared in terms of various errors and localization efficiency for several values of target and anchor nodes. The simulations results signify that the proposed BOA variant 2 is superior to the proposed BOA variant 1 and existing BOA in terms of several errors. The node localization based on the proposed BOA variant 2 is more effective as it takes less time to perform computations and has less mean localization error than the proposed BOA variant 1, BOA and other existing optimization algorithms.

Distributed wireless sensor node localization based on penguin search optimization

Wireless sensor networks (WSNs) have become popular for sensing areas-of-interest and performing assigned tasks based on information on the location of sensor devices. Localization in WSNs is aimed at designating distinct geographical information to the inordinate nodes within a search area. Biologically inspired algorithms are being applied extensively in WSN localization to determine inordinate nodes more precisely while consuming minimal computation time. An optimization algorithm belonging to the metaheuristic class and named penguin search optimization (PeSOA) is presented in this paper. It utilizes the hunting approaches in a collaborative manner to determine the inordinate nodes within an area of interest. Subsequently, the proposed algorithm is compared with four popular algorithms, namely particle swarm optimization (PSO), binary particle swarm optimization (BPSO), bat algorithm (BA), and cuckoo search algorithm (CS). The comparison is based on two performance metrics: localization accuracy and computation time to determine inordinate nodes. The results obtained from the simulation illustrate that PeSOA outperforms the other algorithms, achieving an accuracy higher than 30%. In terms of computation time to determine inordinate nodes, the proposed algorithm requires 28% less time (on average) than the other algorithms do.

Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

Air pollution is one of the major concerns considering detriments to human health. This type of pollution leads to several health problems for humans, such as asthma, heart issues, skin diseases, bronchitis, lung cancer, and throat and eye infections. Air pollution also poses serious issues to the planet. Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions. Thus, real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions. The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks. Localization is the main issue in WSNs; if the sensor node location is unknown, then coverage and power and routing are not optimal. This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities. These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants, such as PM2.5 particulate matter, PM10, nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2). The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization. The dataset is divided into training and testing parts based on 10 cross-validations. The evaluation on predicting the air pollutant for localization is performed with the training dataset. Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.

Asynchronous Distributed Cooperative Full-State Observer via Gossip Protocol

Distributed cooperative full-state observers estimate the state of a system based on partial estimation at each local sensor node and information exchange among local estimation. An asynchronously distributed cooperative full-state observer is proposed based on a gossip protocol, randomly selecting communication time and a communication node pair. The designed distributed observer achieves the mean square convergence of estimation error.