Improved Node Localization for WSN Using Heuristic Optimization Approaches

Shieh, Chin‐Shiuh; Sai, Van-Oanh; Lin, Yuh-Chung; Lee, Tsair-Fwu; Nguyen, Trong-The; Le, Quang-Duy

doi:10.1109/nana.2016.58

Cited by 28 publications

(13 citation statements)

References 9 publications

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“…The gathering of data from the network becomes difficult in case when the position as well as identification of sensor nodes is not estimated. The optimization issue faced within WSNs is node localization which is mainly caused due to the estimation of positions of nodes [11]. Several optimization algorithms that are proposed for node localization are compared within this research to evaluate the performances of each other in comparison to each other.…”

Section: IImentioning

confidence: 99%

Hybrid Optimization Approach for Node Localization in Underwater Acoustic Networks

Gupta¹

2019

IJRASET

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The underwater acoustic network is the type of network which is deployed under the deep sea to sense ocean conditions like pressure etc. Due to the presence of limited bandwidth, higher multi-path, higher fading, huge time-variations as well as Doppler shifts, it is difficult to perform high-speed communication within underwater acoustic channels. Within the sea waters, the propagation of electromagnetic waves is very poor. Originally for the terrestrial wired and wireless channels, the communication techniques were designed. Thus, in order to make them appropriate for underwater channels, there is a need to modify these techniques. In the previous research, the fry fly algorithm is applied for the node localization. In the fry fly algorithm the optimal value is calculated for the node localization. In this research work, distance based technique is applied for the node localization. The proposed and existing algorithms are implemented in MATLAB. The simulation results show that proposed algorithm performs well in terms of certain parameters.

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Section: IImentioning

confidence: 99%

Hybrid Optimization Approach for Node Localization in Underwater Acoustic Networks

Gupta¹

2019

IJRASET

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“…In general, the range-based method has higher positioning accuracy and higher complexity than the range-free method. There exist many range-based positioning methods, such as those based on time of arrival (TOA) [6,7], time difference of arrival (TDOA) [8][9][10], received signal strength indicator (RSSI) and so on [11,12]. As for range-based localization methods, they consist of two steps in the positioning process: distance estimation and position calculation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Localization Algorithm Based on RSSI and Multilateration for Indoor Environments

Yuan

Yue

et al. 2022

Electronics

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Indoor localization algorithms based on the received signal strength indicator (RSSI) in wireless sensor networks (WSNs) have higher localization accuracy than other range-free methods. This paper considers indoor localization based on multilateration and averaged received signal strength indicator (RSSI). We propose an approach called weighted three minimum distances method (WTM) to deal with the poor accuracy of distances deduced from RSSI. Using a practical localization system, an experimental channel model is deduced to assess the performance of the proposed localization algorithm in realistic conditions. Both simulated data and measured data are used to verify the proposed method. Compared with nonlinear least squares (NLS), Levenberg–Marquardt algorithm (LM) and semidefinite programming method (SDP), simulations show that the proposed method exhibits better localization accuracy but consumes more calculation time.

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“…For maximizing the throughput for SUs avoiding the collision trade‐off between sensing accuracy and transmission of data is described in Zhang et al and Al‐Doseri . For maximizing the utilities and many multiobjective functions, scientists took help of metaheuristic algorithms like genetic algorithm (GA) in Balieiro et al, Kamran and Moessner, and Khalid and Anpalagan . To solve the problem of channel allocation, game theory technique has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Guided joint spectrum sensing and resource allocation using a novel random walk grey wolf optimization for frequency hopping cognitive radio networks

Karthikeyan

Srividhya

Kundu

2019

Int J Communication

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Summary Effective use of unused licensed spectrum by secondary nodes without causing any harmful interference to the primary users is one of the challenging task, and in this paper, a detailed description of joint spectrum sensing and resource allocation in cognitive radio (CR) networks is discussed. The CR system utility is maximized by joint spectrum sensing and channel resource allocation scheme based on random walk grey wolf optimization (RW‐GWO) algorithm for frequency hopping cognitive radio‐based networks. In this paper, the nodes will sense the presence of primary users in the same channel and move to another channel in the guidance of a guide node (GN) if primary signal is detected in the channel. In order to achieve collision‐free communication, primary signals and secondary signals use the channels strategically and the mechanism is described in this paper. RW‐GWO is used to derive the optimum sensing and data transmission schedules. It selects the sensing nodes to sense the spectrum and other nodes take part in transmitting the data. GNs not only guide the nodes to hop to the next channel if the primary channel is detected, but also distribute the nodes in different available secondary hopping channels. Simulation results show that reliable spectrum sensing and efficient channel allocation can be achieved in our proposed algorithm.

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Improved Node Localization for WSN Using Heuristic Optimization Approaches

Cited by 28 publications

References 9 publications

Hybrid Optimization Approach for Node Localization in Underwater Acoustic Networks

Hybrid Optimization Approach for Node Localization in Underwater Acoustic Networks

A Novel Localization Algorithm Based on RSSI and Multilateration for Indoor Environments

Guided joint spectrum sensing and resource allocation using a novel random walk grey wolf optimization for frequency hopping cognitive radio networks

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