A Novel Efficient Heuristic Based Localization Paradigm in Wireless Sensor Network

Sruthi, P. V.; Sahadevaiah, Kuncha

doi:10.1007/s11277-021-08091-1

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…In [22] and [23], heuristic-derived methodologies were introduced providing indoor coverage prediction for indoor dominant path models. However, heuristic solutions are typically designed for specific problems, and might not generalize well to other scenarios or variations of the problem, making them inconsistent and unreliable in critical applications.…”

Section: Related Workmentioning

confidence: 99%

Radio Environment Map Construction Based on Privacy-Centric Federated Learning

Khan,

García,

Hwang

et al. 2024

IEEE Access

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In today's digital age, coverage prediction is essential for optimizing wireless networks and improving user experience. While numerous path loss models and advanced machine learning algorithms have been developed to achieve high prediction performance, they predominantly operate within a centralized learning paradigm. While effective, this conventional approach often suffers from scalability and privacy limitations that are critical to the successful deployment of wireless maps. Conversely, in this paper, we propose a novel decentralized approach based on a federated learning long short-term memory (LSTM) model to accurately predict network coverage in indoor environments. The proposed FedLSTM is a method that allows multiple users, or clients, to train the model without sharing their personal data directly with a central server. In an experimental setup, we used real data collected from numerous clients moving along different paths. The FedLSTM model is evaluated in terms of root mean square error (RMSE), mean absolute error (MAE), and R2. Furthermore, compared to a centralized counterpart, FedLSTM shows a slight increase in RMSE from 2.4 dBm to 2.5 dBm and an increase in MAE from 1.7 dBm to 1.9 dBm. In addition, we evaluate the proposed FedLSTM considering variations in the number of participating clients and the number of local training epochs. The results show that even devices with limited computational power can meaningfully contribute to the training of the federated model, with fewer epochs achieving competitive results. Graphical analyses of the radio environment maps (REMs) generated by both FedLSTM and the centralized LSTM highlight their similarities. However, FedLSTM provides client privacy while reducing communication overhead and server strain.INDEX TERMS Radio environment map (REM), coverage prediction, received signal strength indicator (RSSI), LiDAR sensor, federated learning.

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Section: Related Workmentioning

confidence: 99%

Radio Environment Map Construction Based on Privacy-Centric Federated Learning

Khan,

García,

Hwang

et al. 2024

IEEE Access

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“…From this technique, localization errors is minimized up to 0.21 and the time of convergence is 0.1834s. Sruthi et al [43] have developed a method to reduce the path loss by Grey Wolf Ant Lion Recurrent (GWALR) localization in WSN and find the position of every unknown nodes and improve the Received Signal Strength (RSS) to reduce the localization error. The location of every node is tracked by the function of GWALR.…”

Section: Review Based On Metaheuristics Technique For Wsn Localizationmentioning

confidence: 99%

Systematic Review on WSN Localization using Metaheuristic and Learning Methods Based on AI Techniques

Mehta,

Mahajan

2023

ijmst

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The localization process in Wireless Sensor Networks (WSNs) is carried out for finding the exact position of the sensor node. WSN are applied in various number of applications like monitoring, humidity, hospitality and so on. Hundreds to thousands of nodes in the sensor sense the data to the base station. Thousands of nodes in the WSN makes the installing of Global processing System (GPS). It is more costly and the GPS not provide the exact location in the indoor environment. In spite of dense network, manual configuration is not possible in the indoor environment. This cause a raise in problem to identify the exact position without GPS or any manual configuration methods. This paper presents the methods to overcome the above issues in WSN localization, by path planning, metaheuristic and deep learning are the techniques having high scopes in the industries, mining etc. Many times sensing of data are restricted by accuracy, energy, power and efficiency in the nodes. This paper discuss the WSN localization process classifications and the methodologies in localization. Localization issues are minimized by various techniques like ToA, TDoA and RSSI. The types of localization, its applications are also discussed.

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“…The research in [119] aimed to develop a novel grey wolf ant lion recurrent (GWALR) localization model in WSN to find the location of each unknown node. Moreover, the fitness function of GWALR is utilized to track the location of each node.…”

Section: Deployment and Localization Solutions Based Ai Techniques In...mentioning

confidence: 99%

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

et al. 2022

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The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in various fields such as manufacturing, smart city, transport, health and the Internet of Things, by providing pervasive real-time applications. In this paper, we analyze the existing research trends of Coverage, Deployment and Localization challenges in WSN with respect to Artificial Intelligence (AI) methods for WSN enhancement. We present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. This would guide the reader towards an understanding on upto-date applications of AI methods with respect to different WSN challenges. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the Coverage, Deployment and Localization challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new directions for future research.

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A Novel Efficient Heuristic Based Localization Paradigm in Wireless Sensor Network

Cited by 8 publications

References 26 publications

Radio Environment Map Construction Based on Privacy-Centric Federated Learning

Radio Environment Map Construction Based on Privacy-Centric Federated Learning

Systematic Review on WSN Localization using Metaheuristic and Learning Methods Based on AI Techniques

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

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