On Improved DV‐Hop Localization Algorithm for Accurate Node Localization in Wireless Sensor Networks

Shen, Shikai; Yang, Bin; Qian, Kaiguo; She, Yumei; Wang, Wu

doi:10.1049/cje.2019.03.013

Cited by 34 publications

(20 citation statements)

References 17 publications

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“…Node positioning is an important part of these applications, and also the premise and basis. WSN can transmit information more accurately only by determining its position coordinates [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An improved range-free location algorithm for industrial wireless sensor networks

Wang

Liu

et al. 2020

J Wireless Com Network

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In the traditional DV-Hop location algorithm, the number of hops within the communication radius is counted as one hop. However, in the case of uneven distribution of nodes, this method will bring a large error to the calculation of hop distance. In order to solve the above problems, an improved DV-Hop location algorithm with multiple communication radius is proposed in this paper. Based on the multi-communication radius location algorithm, the method uses the cosine theorem to correct the hop number and estimated the average hop distance, and optimizes the estimated distance of the unknown node. The algorithm uses multiple communication radius to broadcast the position to obtain the minimum number of hops between the unknown node and the beacon node and adjusts the estimated hop distance by using the cosine theorem after estimating the good hop distance, and finally uses the trilateration method, or the maximum likelihood estimation method estimates the position coordinates. The experimental results show that compared with the traditional DV-Hop algorithm and DDV-Hop algorithm, the improved DV-Hop algorithm improves the positioning accuracy and reduces the average positioning error of the unknown nodes.

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Section: Introductionmentioning

confidence: 99%

An improved range-free location algorithm for industrial wireless sensor networks

Wang

Liu

et al. 2020

J Wireless Com Network

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“…The proposed model ODR thrust upon these two points. To examine the proposed ODR significance, it is closely studied about a recent projected Improved DV-Hop (IDV) algorithm by S. Shen et al [18]. The algorithm [18] employs hyperbolic function based upon the distance equations of an unknown node and an anchor node, as explained below.…”

Section: Error Analysis Of Dv-hop Algorithmmentioning

confidence: 99%

“…4. Improved DV-Hop Algorithm (IDV) [18] In this algorithm, localization has been improved by modifying the hop size. The correction factor (∅ p ) for hop size of an arbitrary anchor node p is obtained by calculating a difference of the distance values obtained as per DV-Hop distance estimation method and the actual distance in-between every anchor node pair, shown by the following equation-…”

Section: Error Analysis Of Dv-hop Algorithmmentioning

confidence: 99%

“…Further to convert the hop size into the distance, IDV [18] does not consider all the anchor nodes. Instead, it finds out the hop counts which must be traversed from the unknown node to have at least three anchor nodes only.…”

Section: Error Analysis Of Dv-hop Algorithmmentioning

confidence: 99%

“…Though the suggested modifications by IDV [18] can reduce the localization error considerably it draws some shortfalls also. The hop size correction factor ∅ p contributes a noteworthy complexity of the order of m 2 (if there are m number of anchor nodes only).…”

Section: Error Analysis Of Dv-hop Algorithmmentioning

confidence: 99%

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Optimized Distance Range Free Localization Algorithm for WSN

Kumar

Batra

Authorea

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DV-Hop and its various improvements overexpose hop size and hop count for localization. Whereas hop size is always erroneous and hop path is not a straight line, which leads to a faulty location estimation. The proposed model Optimized Distance Range Free (ODR) localization algorithm limits the use of hop size and hop count to approximate nearly a straight line distance between a known and an unknown node without additional hardware and without increasing extra communication. The refrain use of hop size and hop count improves localization accuracy of ODR and makes it robust against those network variables which affect the hop size accuracy adversely. In fact ODR modifies the last two steps of DV-Hop. DV-Hop finds hop size in its second step. Here ODR rectifies this hop size and then a centroid is obtained from the minimum distant anchor nodes for an unknown node. Now a minimum possible distance known as base distance is estimated with a routing table assistance. In the last step DV-Hop uses least square regression to localize, while ODR exploits linear optimization to comprehend the base distance for localization. The paper establishes ODR analytically and rugged with ranging error of the omnidirectional antenna coverage pattern experimentally.

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Hybrid cuckoo–red deer algorithm for multiobjective localization strategy in wireless sensor network

Moorthy

Joe²

2021

Int J Communication

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Summary For the past 5 years, localization has become an emerging area in the field of wireless sensor networks (WSNs), which can be applicable for diverse applications, such as outdoor environments and monitoring of objects located in indoor environments. The major constraint in localization is allocating a location for each node as diverse sensor nodes are employed to retrieve the information in WSN. Recent research works in node localization have generally moved by multihop range‐free localization approaches for achieving high accuracy through reducing the localization error among the actual and estimated position of nodes. The conventional approaches are aimed to improve the localization accuracy without considering efficiency concerned with algorithm's convergence time and energy costs. The main intent of this paper is to model the WSN localization model, which focuses on the localization of anchor nodes concerning the target nodes. The multiobjective function is developed by considering the distance function, received signal strength (RSS), and energy. Here, a hybrid metaheuristic algorithm named hybrid cuckoo–red deer algorithm (HC‐RDA) with the integration of red deer algorithm (RDA) and cuckoo search algorithm (CSA) is adopted for localization of unknown nodes. The fitness function of the developed model is the minimization of the derived multiobjective function. Both simulation studies and theoretical analysis demonstrate that the proposed approach can improve localization performance with a better convergence rate, thus ensuring high localization accuracy compared to conventional metaheuristic models.

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On Improved DV‐Hop Localization Algorithm for Accurate Node Localization in Wireless Sensor Networks

Cited by 34 publications

References 17 publications

An improved range-free location algorithm for industrial wireless sensor networks

An improved range-free location algorithm for industrial wireless sensor networks

Optimized Distance Range Free Localization Algorithm for WSN

Hybrid cuckoo–red deer algorithm for multiobjective localization strategy in wireless sensor network

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