Mobile target localization based on iterative tracing for underwater wireless sensor networks

Guo, Ruolin; Qin, Danyang; Zhao, Min; Xu, Guangchao

doi:10.1177/1550147720940634

Cited by 12 publications

(12 citation statements)

References 47 publications

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“…

\in^{e}

represents estimated acceleration. Angle of ray represented by

α

is given by the following equation 16 :

({, \begin{matrix} α goodbreak= \frac{1}{2} \in^{e} t_{d}^{2} \\ ∆ t goodbreak= r_{2} goodbreak+ t \end{matrix})

The propagation delays r 1 and r 2 are estimated using the following equation 16 :

({, \begin{matrix} r_{1} goodbreak= \frac{italicσμ - θ t_{d} ((), italicRV goodbreak+ μ) - α}{2 italicμθ} \\ r_{2} goodbreak= \frac{italicσμ - θ t_{d} ((), italicRV goodbreak+ μ) + α}{2 italicμθ} \end{matrix})

Here,

θ = italicclock skew

;

σ

is the time difference at ordinary node;

μ

is average propagation speed;

t_{d}

is time difference at reference node.…”

Section: Proposed Solutionmentioning

confidence: 99%

“…e represents estimated acceleration. Angle of ray represented by α is given by the following equation 16 :…”

Section: Estimation Of Propagation Delaymentioning

confidence: 99%

“…Localization accuracy is also disturbed by the multipath propagation and Doppler frequency shift. Hence, the terrestrial node localization approaches cannot be directly applied to UASNs 16 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A range based node localization scheme with hybrid optimization for underwater wireless sensor network

Nain

Goyal

Awasthi

et al. 2022

Int J Communication

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A great interest among researchers to establish Underwater Wireless Sensor Networks (UWSN) with mobile nodes have been created in recent past because of numerous potential applications in aquatic science. In comparison of radio communication, acoustic communication has lower bandwidth and larger propagation delay. This enacts additional restrictions on any localization algorithm. Existing range-based approaches involve increased localization latency and error even with large deployment of anchor nodes. Some of the optimized localization techniques involve higher energy consumption and cost. In order to solve these issues, a hybrid optimized localization technique is proposed that can accurately localize mobile nodes in UWSN. In this algorithm, a fitness function is concluded based on the number of hops from the anchor node, Time of Arrival (ToA) distance estimation error and transmission delay. In comparison of existing localization techniques, proposed algorithm helps in reducing the energy consumption, localization error, localization delay and localization cost and experiment results proved the same.

show abstract

“…

\in^{e}

represents estimated acceleration. Angle of ray represented by

α

is given by the following equation 16 :

({, \begin{matrix} α goodbreak= \frac{1}{2} \in^{e} t_{d}^{2} \\ ∆ t goodbreak= r_{2} goodbreak+ t \end{matrix})

The propagation delays r 1 and r 2 are estimated using the following equation 16 :

({, \begin{matrix} r_{1} goodbreak= \frac{italicσμ - θ t_{d} ((), italicRV goodbreak+ μ) - α}{2 italicμθ} \\ r_{2} goodbreak= \frac{italicσμ - θ t_{d} ((), italicRV goodbreak+ μ) + α}{2 italicμθ} \end{matrix})

Here,

θ = italicclock skew

;

σ

is the time difference at ordinary node;

μ

is average propagation speed;

t_{d}

is time difference at reference node.…”

Section: Proposed Solutionmentioning

confidence: 99%

“…e represents estimated acceleration. Angle of ray represented by α is given by the following equation 16 :…”

Section: Estimation Of Propagation Delaymentioning

confidence: 99%

See 1 more Smart Citation

A range based node localization scheme with hybrid optimization for underwater wireless sensor network

Nain

Goyal

Awasthi

et al. 2022

Int J Communication

View full text Add to dashboard Cite

show abstract

“…In the game, the sensor node performs as ahead, considering the aspects like energy depletion and 'two-hop anchor nodes while the anchor nodes perform as numerous followers, taking into account their capability to find position of sensor nodes and their energy depletion. Guo et al [20] centred on iterative tracing discussed a mobile target localization technique for UWSN. Four components: first, data gathering, then uneven location assessment is performed, followed by evaluation and decompensation of propagation delay, and finally, the formation of node localization through iteration are recognized in iterative tracing.…”

Section: Fig 3 Nodes With Direct and Indirect Message Passingmentioning

confidence: 99%

“…Efficient Range-Free Localization Algorithm (EERLA) [11] and Mobile Target Localization, based on Iterative Tracing (ITMTL) [20] scheme, are used to assess the effectiveness of the proposed method. For this, a varying number of sensor nodes are taken as 25, 50, 75, 100 and 125 in the area of 1000m x 1000m x 100 m. The system is simulated for 100 seconds.…”

Section: Simulation Parametersmentioning

confidence: 99%

Energy Efficient Localization Through Node Mobility and Propagation Delay Prediction in Underwater Wireless Sensor Network

Nain

Goyal

2021

Wireless Pers Commun

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The long propagation delay of sensor nodes' messages and flexibility are the mobile target localization scheme's principal tasks in Underwater Wireless Sensor Network (UWSN). As propagation delay is one of the principal aspects, disturbing the time harmonisation of underwater sensor nodes, a long-lasting delay must be assessed and adjusted to attain precise localization. In this article, we suggest an energy-efficient localization scheme based on Mobility and Propagation delay prediction. The system model comprises Surface buoys that drift on the water surface, Anchor nodes which drift at various water depths and Ordinary nodes that are extensively disseminated at different profundity. Anchor node mobility prediction procedure is intended to analyze and record its speed at every localization period. Then, the propagation delay is expected and compensated to attain precise localization.The ordinary nodes make localization, utilizing the expected speed vectors got from the anchor nodes. The replicating outcomes, exhibit that the suggested method increases overall energy efficiency.

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Hybrid localization scheme using K‐fold optimization with machine learning in WSN

Yadav

Sharma

Rishiwal

2022

Int J Communication

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Summary Node localization technology can identify and track nodes, making observing data more relevant; for example, information received at the sink node would be useless to the client if node localization data from the sensor region were not included. Localization is described as determining the location of unknown sensor nodes named destination nodes applying the recognized location of anchor nodes based on measurements such as time difference of occurrence, time of occurrence, angle of occurrence, triangulation, and maximum probability. The purpose of node localization is to assign coordinates points to all sensor nodes arbitrarily put in the monitoring region and have an unknown location. Localization of nodes is essential to account for the cause of events that help group sensor querying, routing, and network coverage. In this paper, data transmission among the nodes is done by comparing the received signal strength indicator (RSSI) value with the supervised learning value. If the RSSI value is less than the supervised learning value, the data transmission takes place; else, no transmission. This paper proposes a hybrid localization scheme that effectively uses K‐fold optimization with supervised learning and gives good results for distance error and RSSI/energy efficiency. The proposed scheme can effectively detect the optimal path for data transmission, node localization for the destination, and overall performance enhancement using threshold decision making.

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Mobile target localization based on iterative tracing for underwater wireless sensor networks

Cited by 12 publications

References 47 publications

A range based node localization scheme with hybrid optimization for underwater wireless sensor network

A range based node localization scheme with hybrid optimization for underwater wireless sensor network

Energy Efficient Localization Through Node Mobility and Propagation Delay Prediction in Underwater Wireless Sensor Network

Hybrid localization scheme using K‐fold optimization with machine learning in WSN

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