Wireless Sensor Networks Localization Research Topics

Localization in Wireless Sensor Network (WSN) is used to find the exact location of a sensor node in a network of a particular area. To get more knowledge about this technology continue reading this paper till end.

1. Define Localization in WSN

WSN Localization is the process of identifying exact location of a sensor node from a particular area. To make easy for users and applications to know about the collected data by a particular sensor from a specific location, is the main motive why localization is introduced. This is done with the help of creating special awareness in the network. This type of identification is needed in many WSN applications like target tracking, computing context aware and environmental monitoring.

2. What is Localization in WSN?

Localization is a method in which specific place of a node is located in a wireless network. The sensor nodes will gather information from a location for processing it, so it is important to know about the particular location from where the data has been collected.

3. Where Localization in WSN is used?

This technology is used in WSN has been applied in many industries where identifying the particular place of a sensor node is very much important, from which some of them are listed below:

Environmental monitoring: For mapping the environmental conditions data collected by a particular sensor from an area is very important.

Target surveillance and tracking: For tracking a moving object we should know about the exact position of node.

Industrial application: For the purpose of controlling and monitoring.

Healthcare: For tracking and monitoring patient health records.

4. Why Localization in WSN technology is proposed? Previous Technology Issues

This technology was proposed in order to make improvement in the functionality of a sensor network by segmenting the collected data. To know about the exact location of particular sensor node is important in certain industries like Target tracking, Healthcare, Environmental monitoring and Industrial automation. The performance, value and versatility of WSN have been increased with the help of localization by identifying the position of sensor.

Some of the technological issues related with localization in WSN, which was faced by it earlier have been listed below:

Inaccuracy and Noisy measurements: Issues such as environmental factors, hardware limitations or interference can affect the measurements of signal strength, arrival angle or flight time.

Limited resources: Basically WSN nodes are resource-constrained; they have only limited processing capabilities, memory and power. The algorithm for localization should be designed properly in order to function effectively.

Node mobility: When the nodes are mobile, it becomes very difficult to finds its location.

5. Algorithms / Protocols

The algorithms provided for Localization in WSN to overcome the previous issues faced by it are: “Artificial Plant Community Whale Optimization” (APCWO)-DV-Hop, “Sleep-Awake Energy-Efficient Distributed” (SEED), Fuzzy logic and “Improved Hydro Vector Flow Optimizer” (IHVFO)

6. Comparative study / Analysis

The algorithms mentioned here in this paper are used to overcome the issues of localization which were mentioned in the previous papers.

Sleep-Awake Energy-Efficient Distributed (SEED): This algorithm is used in clustering.

Improved Hydro Vector Flow Optimizer (IHVFO): This algorithm is used to detect the positions of node.

Artificial Plant Community Whale Optimization (APCWO)-DV-Hop: This algorithm serves as localization algorithm.

Fuzzy logic: This algorithm is used for routing.

7. Simulation results / Parameters

The approaches which were proposed to overcome the issues faced by localization are tested using different methodologies to analyze its performance. The comparison is done by using metrics like Positioning accuracy, Throughput, Computation time, Number of nodes vs. localization error, ratio of Packet loss, Localization efficiency, Positioning error, Energy consumption and ratio of Packet delivery.

8. Dataset LINKS / Important URL

Here are some of the links provided for you below to gain more knowledge about Localization in WSN which can be useful for you:

• https://eprints.whiterose.ac.uk/186485/1/adhoc.pdf
• https://www.mdpi.com/1424-8220/22/24/9927
• https://www.sciencedirect.com/science/article/pii/S1110016823008591
• https://www.sciencedirect.com/science/article/abs/pii/S0165011421002682
• https://link.springer.com/article/10.1007/s11227-021-04278-2

9. Localization in WSN Applications

Localization Technology in WSN can be used in several applications in various industries, from which some of them are listed here like: Health monitoring, Environmental monitoring, Smart home and buildings, Industrial monitoring, Target surveillance and tracking, supply chain and Logistics.

10. Topology

Topology is the architecture of a network in other words the implementation plan for localization in WSN. The positioning of nodes depends upon the topology which the network opts. Some of the topologies usually used in localization are listed further: Mesh topology, Tree topology, flat topology, Cluster based topology, Hierarchical topology, Anchor based topology and Grid topology.

 

11. Environment

WSN localization is used in the place where accuracy and performance are in top priority. The factors which affect finding the position of a node are signal propagation, obstacles and physical layout. The localization result will be more accurate in the environment where there are fewer obstacles. The one with more obstacles produce poor result because of signal attenuation and multipath effects. The environment becomes worse for localization when the obstacle is moving or when changes in signal propagation occur. Adaptation to the environment is more important for producing a meaningful result of Localization in WSN.

12. Simulation Tools

Here we provide some simulation software for previous works, which is established with the usage of NS tool with version 3.36 or above version.

13. Results

When you complete reading this paper, now at this stage you can get a clear idea of this technology like what actually it is, where it is implemented, the algorithms used in it, topologies followed by it and many more.

Wireless Sensor Networks Localization Research Ideas

1. Node localization in WSN using the slime mold algorithm
2. A Node Movement Localization Scheme in WSN using PSO
3. Q-Learning Based Optimized Localization in WSN
4. Machine Learning Based Techniques for Node Localization in WSN: A Survey
5. Possibilities of using WSN for object localization and analysis of acquired data
6. Improved Distance vector based Kalman Filter localization algorithm for wireless sensor network
7. Localization Accuracy Improvement in WSNs using Trilateration Techniques
8. A Distributed Gradient Descent Method for Node Localization on Large-Scale Wireless Sensor Network
9. A Lidar-Assisted Self-Localization Technology for Indoor Wireless Sensor Networks
10. Distributed Localization Based on the Fusion of K-Means Clustering and SOCP Method in Wireless Sensor Networks
11. Node localization and performance analysis using Pelican Optimization Algorithm in WSN
12. Performance Analysis of an Optimization-based Localization Algorithm for the 3D Spiral Deployed Wireless Sensor Network
13. Complexity Reduction for Hybrid TOA/AOA Localization in UAV-Assisted WSNs
14. Privacy-Preserving Distributed Iterative Localization for Wireless Sensor Networks
15. Enhanced Optimization-based Node Localization Scheme for WSN
16. A Study and Analysis of a New Hybrid Approach for Localization in Wireless Sensor Networks
17. Deep Learning-Based Device-Free Localization in Wireless Sensor Networks
18. An optimum localization approach using hybrid TSNMRA in 2D WSNs
19. Target localization using information fusion in WSNs-based Marine search and rescue
20. Adaptive mean center of mass particle swarm optimizer for auto-localization in 3D wireless sensor networks
21. A DV-Hop optimization localization algorithm based on topological structure similarity in three-dimensional wireless sensor networks
22. Secure localization techniques in wireless sensor networks against routing attacks based on hybrid machine learning models
23. A review of localization algorithms based on software defined networking approach in wireless sensor network
24. Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithm
25. Barycentric coordinate-based distributed localization for wireless sensor networks subject to random lousy links
26. Retraction Note to: ERTC: an enhanced RSSI based tree climbing mechanism for well-planned path localization in WSN using the virtual force of Mobile Anchor Node
27. Correction to: Range free localization in WSN against wormhole attack using Farka’s Lemma
28. Unveiling the Cutting Edge: A Comprehensive Survey of Localization Techniques in WSN, Leveraging Optimization and Machine Learning Approaches
29. Secured DV-Hop localization scheme for WSN in environmental monitoring
30. WSN node localization algorithm of sparrow search based on elite opposition-based learning and Levy flight
31. Optimized localization in large-scale heterogeneous WSN
32. Localization in wireless sensor networks and wireless multimedia sensor networks using clustering techniques
33. Metaheuristic Optimization Based Node Localization and Multihop Routing Scheme with Mobile Sink for Wireless Sensor Networks
34. DV-Hop based localization algorithm using node negotiation and multiple communication radii for wireless sensor network
35. Wormhole attack detection and recovery for secure range free localization in large-scale wireless sensor networks
36. A Clustering-Based 3D Localization in Wireless Sensor Networks Using RSSI and AoA
37. A new hybrid localization approach in wireless sensor networks based on particle swarm optimization and tabu search
38. RSSI-based geometric localization in wireless sensor networks
39. Optimized Localization Learning Algorithm for Indoor and Outdoor Localization System in WSNs
40. Accurate Range-Free Localization with Hybrid DV-Hop Algorithms Based on PSO for UWB Wireless Sensor Networks
41. Improved Wireless Sensor Network Localization Algorithm Based on Selective Opposition Class Topper Optimization (SOCTO)
42. Localization algorithm for anisotropic wireless sensor networks based on the adaptive chaotic slime mold algorithm
43. A three-dimensional wireless sensor network with an improved localization algorithm based on orthogonal learning class topper optimization
44. Modified Rat Swarm Optimization Based Localization Algorithm for Wireless Sensor Networks
45. A high-accuracy and low-energy range-free localization algorithm for wireless sensor networks
46. Optimal Performance Evaluation of Localization of Sensor Nodes in Wireless Sensor Networks
47. A Novel Hybrid Approach for Localization in Wireless Sensor Networks
48. Distributed Algorithm for Localization of Localizable Wireless Sensor Networks
49. Performance Comparison of Localization Techniques in Term of Accuracy in Wireless Sensor Networks
50. Localization of Wireless Sensor Network Using Neural Network and Line Segment Secondary Calibration