Location Of Sensor Nodes Research Articles

Localization in WSN based on Area of field and mobility : A Survey

Abstract— Localization in Wireless Sensor Networks (WSNs) is process to determine the locations of sensors connected within a network. Several applications need to determine the location of sensor nodes in the network to help increasing network performance. Some applications, such as item tracking, military operations, routing protocols, robotics, and inventory management, are made feasible by sensor network’s ability to accurately locate the sensor nodes. For each of these applications, different criteria for position estimation. These are speed, accuracy, and reliability. In this paper we going to classify the localization algorithms with a new perspective based on three criteria (area of fields, cooperation between nodes and node’s mobility). Following along this paper may give’s an idea to the researchers to develop efficient algorithms to localize nodes with accuracy adapting to different techniques with respect to the geographic area and anchor type to be designed

Sensor node localization with improved hop-size using PSODESA optimization

Sensor node localization with improved hop-size using PSODESA optimization

Efficient sensor node localization in precision agriculture: an ANN based framework

Efficient sensor node localization in precision agriculture: an ANN based framework

Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithm

In the recent age of technological advancements, wireless sensor networks are an important application for smart modernized environments. In WSNs, node localization has been an issue for over a decade in the research community. One of the goals of localization in a wireless sensor network is to localize sensor nodes in a two-dimensional plane. Localization in wireless sensor networks helps to supply information to aid decision-making from the aggregated data that are sent from packets to base stations. Internet of Things with the use of Global Positioning Systems for tracking sensor zones is not a cost-effective means of solution. In the extant literature, there have been a variety of algorithms to identify unknown sensor locations in wireless sensor networks. This research paper aims to address the problem of determining the location of the sensor node at the base station with minimum localization error when the data between the nodes is transmitted wirelessly. To detect the location of an unknown sensor node packets sent to the destinations, the total number of anchor nodes, location error and distance estimation error were considered. The DEEC-Gauss Gradient Distance Algorithm has a lower localization error than the Weighted Centroid Localizations algorithm, Compensation Coefficient algorithm, DV-Hop algorithm, Weighted Hyperbolic algorithm and Weighted Centroid algorithm for the same ratio of anchor nodes and WSN configuration. According to the study's findings, the DGGDEA has an average localization error of 11% for anchor nodes (20-80), and an average localization error of 11.3% for anchor nodes 200-450. Hence, the DEEC-Gaussian Gradient Distance Elimination Algorithm (DGGDEA) showed higher accuracy with comparison to the modern-day approaches.

Mobile Anchor Assisted Effective Node Localization Algorithm for Wireless Sensor Networks

Wireless Sensor Network (WSN) is collection of thousands of randomly, spatially distributed low-cost Sensor Nodes (SN) deployed normally in harsh environments. These deployed SNs collects data and delivers to Base Station (BS) located in a remote region. In a sensor network it is necessary to know about the location of sensor node and data collected by that node as it has a strong impact on overall performance of the WSN. An algorithm to transfer data using energy efficient mobile anchor based localization mechanism is presented in this paper. Firstly, the distance between anchor node and unknown node is determined using Received Signal Strength (RSS) and then using Trilateration method location of the unknown node is identified. Proposed algorithm aims to decrease the consumable energy that improves life of the network. Simulation results are compared with the existing localization technique to evaluate performance of the proposed algorithm in terms of energy consumption, packet delivery ratio and control overheads. The proposed scheme performs better than the existing Mobile Anchor Assisted Localization algorithm based on Regular Hexagon (MAALRH) for WSNs

Distributed Set-Membership Fusion Filtering for Nonlinear 2-D Systems Over Sensor Networks: An Encoding-Decoding Scheme.

In this article, the distributed set-membership fusion filtering problem is investigated for a class of nonlinear 2-D shift-varying systems subject to unknown-but-bounded noises over sensor networks. The sensors are communicated with their neighbors according to a given topology through wireless networks of limited bandwidth. With the purpose of relieving the communication burden as well as enhancing the transmission security, a logarithmic-type encoding-decoding mechanism is introduced for each sensor node so as to encode the transmitted data with a finite number of bits. A distributed set-membership filter is designed to determine the local ellipsoidal set that contains the system state by only utilizing the data from the local sensor node and its neighbors, where the proposed filter scheme is truly distributed with desirable scalability. Then, a new ellipsoid-based fusion rule is developed for the designed set-membership filters in order to form the fused ellipsoidal set that has a globally smaller volume than all local ellipsoidal sets. With the aid of the mathematical induction technique, the set theory, and the convex optimization approach, sufficient conditions are derived for the existence of the desired distributed set-membership filters and the fusion weights. Then, the filter parameters and the fusion weights are acquired by solving a set of constrained optimization problems. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed fusion filtering algorithm.

GAUSS-NEWTON MULTILATERATION LOCALIZATION ALGORITHM IN LARGE-SCALE WIRELESS SENSOR NETWORKS FOR IoT APPLICATIONS

The location information of sensor nodes plays an important role in critical applications like health monitoring, fire detection, and intruder detection. Installing global positioning system (GPS) modules with the sensor node hardware is not a cost-effective solution for knowing the location coordinates. This has lead to rigorous research in defining nascent localization techniques for wireless sensor networks. But, the existing localization techniques use more number of anchor nodes to compute the location coordinates of sensor nodes, and the network deployment becomes costly. This article presents a low complex, range-based localization algorithm called gauss-newton multilateration that uses received signal strength indicator (RSSI) values of the anchor nodes' signals received at the target nodes. The proposed algorithm uses only four static anchor nodes, which are deployed at the corners of the network terrain to locate the sensor nodes with localization accuracy of 90.21% and increased up to 98.59%. Based on the results obtained, the proposed algorithm provides higher localization accuracy, and it is well suited for locating sensor nodes with high accuracy in large scale wireless sensor networks.

A Two-Stage Strategy for UAV-enabled Wireless Power Transfer in Unknown Environments

Due to the outstanding merits such as mobility, high maneuverability, and flexibility, Unmanned Aerial Vehicles (UAVs) are viable mobile power transmitters that can be rapidly deployed in geographically constrained regions. They are good candidates for supplying power to energy-limited Sensor Nodes (SNs) with Wireless Power Transfer (WPT) technology. In this paper, we investigate a UAV-enabled WPT system that transmits power to a set of SNs at unknown positions. A key challenge is how to efficiently gather the locations of SNs and design a power transfer scheme. We formulate a multi-objective optimization problem to jointly optimize these objectives: maximization of UAV's search efficiency, maximization of total harvested energy, minimization of UAV's flight energy consumption and maximization of UAV's energy utilization efficiency. To tackle these issues, we present a two-stage strategy that includes a UAV Motion Control (UMC) algorithm for obtaining the coordinates of SNs and a Dynamic Genetic Clustering (DGC) algorithm for power transfer via grouping SNs into clusters. First, the UMC algorithm enables the UAV to autonomously control its own motion and conduct target search missions. The objective is to make the energy-restricted UAV find as many SNs as feasible without any apriori knowledge of their information. Second, the DGC algorithm is used to optimize the energy consumption of the UAV by combining a genetic clustering algorithm with a dynamic clustering strategy to maximize the amount of energy harvested by SNs and the energy utilization efficiency of the UAV. Finally, experimental results show that the proposed algorithms outperform their counterparts.

Machine learning based efficient routing protocol in wireless sensor network

Data loss and recovery are important factors that directly affect the efficiency of the wireless sensor network (WSN). The wireless channel characteristics have a significant impact on data transmission and reception. On the receiver side, the most difficult tasks are maximizing packet delivery ratio and recovering lost data. In some cases, cyclic redundancy check (CRC) based algorithms can provide better data recovery. The CRC method can be made adaptive by using channel characteristics to correct the error bits. This paper evaluates the performance of the proposed machine learning-based efficient routing protocol (ML-ERP). For data recovery, the CRC with channel impulse response (CIR) prediction based on sensor node location information was used. The data recovery capability of ML-ERP increased the network efficiency in terms of packet delivery ratio. Also, due to less data loss, the energy efficiency of the network was also improved by almost 6 % ove existing protocols.

Performance Analysis of Mobile Anchor Assisted Circularized Localization Scheme in Wireless Sensor Networks

A wireless sensor network is a large network with plenty of nodes for sensing and tracking network changes. High network mobility causes quality issues and may reduce delivery rate. Locating sensor node aids in accurate and loss-free data transmission to the destination. To locate the mobile nodes in a sensor network, a novel Mobile Anchor Assisted Circularized Localization Scheme has been proposed in this work. This proposed method uses a mobile assisted anchor node for determining the location of sensor nodes in a circularized manner. The position of the anchor node is determined by using control messages sent from the base station. Following the identification of the anchor node, the coordinates of nearby nodes are calculated using the circularized position and the Euclidian distance. This localization scheme aids in determining the precise position of the sensor node. Since the data is transmitted between known localized sensor nodes, the drop rate reduces while the delivery rate increases. The simulation result examines the performance of the proposed scheme using Quality of Service parameters such as throughput, drop rate and delivery rate. The proposed model achieves a higher delivery ratio and improves the overall network performance of the sensor network.

Optimized Approach for Localization of Sensor Nodes in 2D Wireless Sensor Networks Using Modified Learning Enthusiasm-Based Teaching–Learning-Based Optimization Algorithm

Wireless Sensor Networks (WSNs) have a wonderful potential to interconnect with the physical world and collect data. Data estimation, long lifespan, deployment, routing, task scheduling, safety, and localization are the primary performance difficulties for WSNs. WSNs are made up of sensor nodes set up with minimal battery power to monitor and reveal the occurrences in the sensor field. Detecting the location is a difficult task, but it is a crucial characteristic in many WSN applications. Locating all of the sensor nodes efficiently to obtain the precise location of an occurrence is a critical challenge. Surveillance, animal monitoring, tracking of moving objects, and forest fire detection are just a few of the applications that demand precise location determination. To cope with localization challenges in WSNs, there is a variety of localization algorithms accessible in the literature. The goal of this research is to use various optimization strategies to solve the localization problem. In this work, a modified learning enthusiasm-based teaching–learning-based optimization (mLebTLBO) algorithm is used to cope with a 2D localization problem applying the notion of an exclusive anchor node and movable target nodes. A modified LebTLBO algorithm seeks to increase overall efficiency by assessing the exploration and exploitation abilities. The computational results reveal that this technique outperforms others with respect to localization errors in a 2D environment of WSN.

Performance of SALP Swarm Localization Algorithm in Underwater Wireless Sensor Networks

In underwater wireless sensor networks, the optimization strategies for localization might be seen as a new boon for the localization of sensor nodes (UWSNs). The techniques for optimization are those that repair the incorrect value, adapt it to the situation, and correct it. Because the algorithm could adapt to the constantly changing environment, it was widely used in terrestrial applications, and the same can be extended to the underwater environment with modifications. To address the localization issue that arises in UWSNs, the Underwater Salp Swarm Algorithm (USSA), a nature-inspired node localization algorithm, has been presented. With the help of this technique, an effort to discover a solution to the localization problem as an optimization problem is considered. The proposed algorithm is accessed in a simulated water environment. The energy is assigned to the anchor well as non-localized nodes, after deploying them in the simulated underwater network. The suggested algorithm is compared with other optimization algorithms, such as UPSO and UBOA, with reference to the computing time, localization accuracy, and the number of localized nodes. It is possible to localize a greater number of nodes in a much faster and more efficient way by considering the proposed algorithm.

An optimized sensor node localization approach for wireless sensor networks using RSSI

An optimized sensor node localization approach for wireless sensor networks using RSSI

A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches

SummaryIn recent years, wireless sensor networks (WSNs) have been widely used in various applications. The localization problem has been identified as one of the biggest problems faced by WSNs. The traditional localization techniques may not be able to handle the issues during the scenario to estimate the location of sensor nodes due to anchor mobility, mobile WSNs, latency, energy harvesting, unfavorable environmental states, and many more issues. However, these issues open the door for the amalgamation of machine learning (ML) and optimization techniques with localization techniques. Motivated by the earlier discussion, we explored various ML and optimization techniques to estimate the location coordinates in a sensor network in this paper. Finally, a comparison of existing ML algorithms concerning optimization techniques has been presented, highlighting their improved outcomes. This research offers a detailed survey by exploring the various parameters for location estimation through tabular forms by incorporating ML and optimized localization techniques. A survey of surveys is also presented to identify the key limitations of existing surveys and to introduce the novelty in the comprehensive study done in this paper. A year‐wise evaluation of ML Techniques with localization (2011–2022) is also discussed and presented over various performance parameters, including energy‐efficiency, accuracy, error, and complexity. This discussion concluded that Hybrid Techniques are least explored for using optimized localization machine learning. Further, a summarized discussion of the various comparison tables paves the path for future research in the area of localization in WSN.

Repulsion-based grey wolf optimizer with improved exploration and exploitation capabilities to localize sensor nodes in 3D wireless sensor network

Repulsion-based grey wolf optimizer with improved exploration and exploitation capabilities to localize sensor nodes in 3D wireless sensor network

Combined sensor selection and node location optimization for reducing the localization uncertainties in wireless sensor networks

Local Positioning Systems (LPS) address the limitations of Global Navigation Satellite Systems in harsh environments. LPS must attain the optimal location of their sensors in space for optimal performance, which is known as the Node Location Problem. In addition, not all the sensors under coverage present the best performance for determining the location. For this purpose, the optimal selection of the best combination of nodes requires addressing the Sensor Selection Problem. In this paper, for the first time in the literature, we present a methodology for the combined optimization of both problems which may attain improved results than separated optimizations. Results show that the consideration of SSP based sensor selection strategies during the NLP suppose a reduction up to 10% in the localization uncertainties with respect to traditional baseline sensor selection strategies of the NLP, thus proving the effectiveness of the devised technique.

Safety DV-hop positioning algorithm of underwater acoustic sensor networks based on Nesterov accelerated gradient descent method

Before sensing and transmitting data, the underwater nodes need to calculate and estimate their location information through anchor nodes with known locations. The ranging-free positioning algorithm DV-hop can be applied to the underwater wireless sensor network in a relatively poor environment, which is not required high positioning accuracy. However, some anchor nodes may be destroyed and utilized to deter unknown nodes’ precise positioning in the underwater confrontation scenario. Therefore, we propose a computationally efficient algorithm for determining the location of sensor nodes in harsh and confrontational underwater environments and resisting non-cooperative network attacks. This algorithm integrates Nesterov accelerated gradient descent (NAG) into the DV-hop positioning algorithm and prunes the data by selecting the measurement inconsistency to get better positioning accuracy and higher security. The simulation experiment confirms that our proposed algorithm has better measurement accuracy in the underwater environment than the existing algorithms.

Coverage hole identification & healing in Wireless Underground Sensor Networks

Wireless Underground Sensor Networks (WUSN) are gaining popularity due to their usage in vast prospective domains. Despite wide potential applications, many research issues in WUSNs need to be dealt with. Coverage Holes in WUSNs are one of the major issues that need attention. Nature-inspired optimization algorithms play a crucial role in modern computing, and for more than three decades, the field has made tremendous progress. Traditional algorithms are being replaced with these and have proved their competitiveness in various domains. In this paper, we have proposed an intelligent optimization algorithm based on the TSA (Tree Seed Algorithm) for patching the holes in the network and deciding on effective patching positions such that it reduces the maximum number of holes in the network. The proposed algorithm makes use of random, best, and spiral tree generation for getting the best optimal locations of sensor nodes to be deployed for healing the target region. First, an energy-efficient deterministic algorithm is applied for hole identification in the network. Then, an optimization-based nature-inspired algorithm is applied to heal the holes. The proposed algorithms and others are simulated through MATLAB and results are computed for the randomly deployed network. The proposed hole healing algorithm MTSA (modified TSA) has been compared with PSO (Particle Swarm Optimization) and TSA algorithms. Simulation results prove the effectiveness and robustness of the proposed algorithm. The proposed hole healing algorithm is suitable for small to large-size networks and reduces the larger number of holes than PSO and the original TSA algorithm.

Enhanced Localization Model in Wireless Sensor Network Using Self Adaptive-Barnacles Mating Optimization

Wireless sensor network (WSN) has more demand nowadays because of their diverse applications. In WSN, sensor node localization has become an interesting topic. The nodes are placed randomly to explore, and the determination of accurate nodes’ positions is required. Fixed cost and energy are the main challenges with the sensor, as the location of all nodes that are not maintained. The main aim of this research work is to plan for a new localization method in WSN using a new meta-heuristic concept. The proposed model focuses on two phases: (a) localizing the target nodes and (b) localizing the rest nodes. The optimal selection of target nodes is initially determined by the proposed meta-heuristic model by considering the displacement among the nodes. For performing the optimal localization of rest nodes regarding anchor nodes, the weight of each anchor node is determined, which is done by recurrent neural network (RNN). Furthermore, an objective function concerning the distance and the weight is derived for localizing the rest nodes. The new algorithm called Self Adaptive-Barnacles Mating Optimization (SA-BMO) is used for performing the optimal node localization. The desired outcome proves the developed method is attaining an increase in efficiency.

An artificial hummingbird algorithm based localization with reduced number of reference nodes for wireless sensor networks

Wireless Sensor Networks (WSN) are widely used in recent years due to the advancements in wireless and sensor technologies. Many of these applications require to know the location information of nodes. This information is useful to understand the collected data and to act on them. Existing localization algorithms make use of a few reference nodes for estimating the locations of sensor nodes. But, the positioning and utilization of reference nodes increase the cost and complexity of the network. To reduce the dependency on reference nodes, in this paper, we have developed a novel optimization based localization method using only two reference nodes for the localization of the entire network. This is achieved by reference nodes identifying a few more nodes as reference nodes by the analysis of the connectivity information. The sensor nodes then use the reference nodes to identify their locations in a distributive manner using Artificial Hummingbird Algorithm (AHA). We have observed that the localization performance of the reported algorithm at a lower reference node ratio is comparable with other algorithms at higher reference node ratios.