A high-accuracy and low-energy range-free localization algorithm for wireless sensor networks

Cui, Huanqing; Wang, Sen; Zhou, Chuanai

doi:10.1186/s13638-023-02246-3

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…This section explores the advantages of the proposed Dv-hop + DDNs correction compared to other state-of-the-art optimization algorithms, including those employed by EFPA-G [ 24 ] and WRCDv-hop [ 25 ]. Many contemporary optimization algorithms, such as those utilized by EFPA-G and WRCDv-hop, leverage intelligent optimizers to estimate the locations of unknown nodes.…”

Section: Simulation and Performance Analysismentioning

confidence: 99%

Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things

Esheh,

Affes

2024

Sensors

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Wireless sensor networks (WSNs) have become widely popular and are extensively used for various sensor communication applications due to their flexibility and cost effectiveness, especially for applications where localization is a main challenge. Furthermore, the Dv-hop algorithm is a range-free localization algorithm commonly used in WSNs. Despite its simplicity and low hardware requirements, it does suffer from limitations in terms of localization accuracy. In this article, we develop an accurate Deep Learning (DL)-based range-free localization for WSN applications in the Internet of things (IoT). To improve the localization performance, we exploit a deep neural network (DNN) to correct the estimated distance between the unknown nodes (i.e., position-unaware) and the anchor nodes (i.e., position-aware) without burdening the IoT cost. DL needs large training data to yield accurate results, and the DNN is no stranger. The efficacy of machine learning, including DNNs, hinges on access to substantial training data for optimal performance. However, to address this challenge, we propose a solution through the implementation of a Data Augmentation Strategy (DAS). This strategy involves the strategic creation of multiple virtual anchors around the existing real anchors. Consequently, this process generates more training data and significantly increases data size. We prove that DAS can provide the DNNs with sufficient training data, and ultimately making it more feasible for WSNs and the IoT to fully benefit from low-cost DNN-aided localization. The simulation results indicate that the accuracy of the proposed (Dv-hop with DNN correction) surpasses that of Dv-hop.

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Section: Simulation and Performance Analysismentioning

confidence: 99%

Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things

Esheh,

Affes

2024

Sensors

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“…A range-based localization scheme and a range-free localization scheme both use anchor nodes (Aldeen et al, 2023;Kaur et al, 2023). It is easier and cheaper to implement the range-free algorithm because it only relies on connectivity between sensor nodes (Cui et al, 2023). The range-free computations include locations of anchors/beacons and hopdistances between nodes and founded on these, the location of a sensor can be estimated.…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of localization range-free algorithms in wireless sensor networks

Edam,

Abubakr,

Rahma

et al. 2024

Int. J. Eng. Sci. Tech

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Localization in wireless sensor networks is essential not only for determining the location but also for routing, managing density, tracking, and a wide range of other communication network functions. There are two main categories of localization algorithms in wireless sensor networks: range-based algorithms and range-free techniques. Localization based on range-free algorithms has benefits in terms of requiring less hardware and energy, making it cost-efficient. This paper examines the impact of beacon nodes on range-free localization algorithms. The findings indicate that ADLA has intermediate localization errors and the best detecting nodes. It also addresses the effect of the number of locators on the algorithm’s efficiency. The findings demonstrate that as the number of locators increased, the number of detected nodes in Centroid also increased. Compared to Centroid, ADLA has the second-best detecting nodes but with better average error. Moreover, it considers the impact of the number of static nodes on range-free localization algorithms, and ADLA achieved the best detection nodes. According to the evaluated results, this paper proposes a hybrid algorithm that combines the Centroid algorithm and Active Distributed Localization Algorithm (ADLA) algorithm. However, combining these two algorithms results in less localization error.

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Improvement of amorphous localization algorithm in WSN using ALO and GWO

Tripathy,

Khilar

2024

Concurrency and Computation

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SummaryThe process of node identification is referred to as localization, and it is rapidly gaining popularity in the field of WSN. Different node identification processes have different findings, benefits, challenges, costs, effectiveness, and applications. In this work, the position error of the Amorphous algorithm is minimized by optimizing the hop size. For optimization of the hop size of the Amorphous algorithm, two different optimization algorithms, such as ALO and GWO, are considered. Proposed Amorphous‐ALO and Amorphous‐GWO provide higher accuracy rates of 33.89% and 4.22% than traditional Amorphous as well as ensemble approaches. Amorphous‐ALO and Amorphous‐GWO provide position errors 2.9161 and 2.9164 respectively, which are very similar. Therefore, to determine the suitable optimization algorithm for Amorphous, the minimum, average, and maximum execution times of Amorphous‐ALO and Amorphous‐GWO are considered. The approach that has less execution time is considered as most suitable for Amorphous. Amorphous‐ALO takes 67.76, 69.60 and 84.24 s for minimum, average and maximum execution whereas Amorphous‐GWO takes 65.17, 65.46 and 65.66 s for minimum, average and maximum execution respectively. As Amorphous‐GWO takes less execution time than Amorphous‐ALO; therefore, GWO is more suitable for optimization in Amorphous algorithm.

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A high-accuracy and low-energy range-free localization algorithm for wireless sensor networks

Cited by 4 publications

References 31 publications

Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things

Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things

Comparative evaluation of localization range-free algorithms in wireless sensor networks

Improvement of amorphous localization algorithm in WSN using ALO and GWO

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