Node localization algorithm for wireless sensor networks using compressive sensing theory

Wei, Yehua; Li, W.; Chen, T.

doi:10.1007/s00779-016-0951-7

Cited by 11 publications

(10 citation statements)

References 14 publications

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“…Figure (6) illustrates that the Average Localization Error decreased with increasing number of unknown nodes since if the number of nodes is more, they are placed on each other's radio range and discover the anchor node. For assessment, 100100 area with 30 anchor nodes and 25m transmission range has been used.…”

Section: The Effect Of the Number Of Unknown Nodes On Localization Errormentioning

confidence: 99%

Design of Optimal Localization Model for Wireless Sensor Network Nodes Using Improved Krill Herd Algorithm

Kamfar¹,

Zandhessami²,

Alborzi³

2021

Preprint

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Nowadays, Wireless Sensor Networks (WSNs) are significantly applied in engineering and scientific research. WSNs consist of a group of distributed space sensors that track the environment's physical conditions and control the collected data at one central location. Examples of these sensors' applications are smart cities, transport, volcano surveillance and environmental activity, earthquake monitoring, medicine, post-disaster response, and military control. Wireless sensor networks have a lot of research issues like access to the media, implementation, time synchronization, network security and localization of the nodes. One of the most critical problems in this network research is the optimum position of the sensors to have access to maximum coverage and increase network life span to decrease maintenance costs, develop and manage the network. One of the main causes of the failure in these networks is running out of sensor battery and replacing them which impose high costs to maintenance and managing of the network. In order to solve the issues related to optimization and localization, researchers have focused on the algorithms like Swarm Intelligence (SI), because they enable us to solve complicated issues of optimization and NP-Hard issues to solve optimization. However, most of these algorithms are specialized for a purpose or a special program, and the majority of the solutions are not compatible with most of the wireless network sensors. The DV-Hop is one of the most popular node algorithms. But the main problem of the DV-Hop is the possibility of error in calculating the assessed distance between the unknown node and the nodes of anchor. Therefore, minimizing this error is the key to improve this algorithm. To reduce the problem of high localization error, two meta-heuristic algorithms have been proposed based on a combination. In this paper, a new optimization method based on a combination of Krill Herd Algorithm (KHA) and Particle Swarm Optimization (PSO) called KHAPSO is suggested to improve DV-Hop. Simulation results in MATLAB 2016 show that the KHAPSO model has a lower mean error compared to the DV-Hop, DV-Hop-KHA and DV-Hop-PSO models. Also, energy consumption in the KHAPSO model is less in comparison to the other models. The KHAPSO model with 400 unknown nodes and 30 anchor nodes was able to reduce energy consumption by about 35% and at the same time 27% reduction in Average Localization Error (ALE) compared to DV-Hop.

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Section: The Effect Of the Number Of Unknown Nodes On Localization Errormentioning

confidence: 99%

Design of Optimal Localization Model for Wireless Sensor Network Nodes Using Improved Krill Herd Algorithm

Kamfar¹,

Zandhessami²,

Alborzi³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Compressed sensing, in particular, has been applied to node localisation in [12][13][14][15] all of which depend on signal intensity distance measurements. In [12,13], anchor-based indoor localisation algorithms are considered, in which the RSS method of distance measurement is employed rather than the TOA method due to the significant multipath effect in complex indoor environments which severely affect TOA measurements. While the present work is not the first to incorporate CS in the WSN localisation context, there are a number of novel aspects to it.…”

Section: Related Workmentioning

confidence: 99%

Compressive sensing for localisation in wireless sensor networks: an approach for energy and error control

Alwan

Hussain

2018

IET wirel. sens. syst.

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Energy efficiency is an important requirement in wireless sensor networks in order to achieve cost-effectiveness and practical implementation. The present work deals with the problem of minimising node power consumption in the context of moving-node localisation and tracking. Time-of-arrival measurements are sent from anchor nodes to a powerful, usually sophisticated, central node, called the fusion centre, where all computations are performed. Low data rates are desirable to economise on node energy but result in sub-optimal localisation accuracy. It makes sense, therefore, to sample measurements at a low data rate while interpolating the data stream at the fusion centre to improve localisation. The localisation error is remarkably reduced and energy efficiency increased by using this conventional sample rate conversion technique. A further improvement in terms of localisation error is achieved using compressive sensing (via random sampling and interpolation), whereby the localisation error function is shown to decrease with higher-average random sampling periods.

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“…Such localization technologies are also called multi-hop range-free localization methods [ 6 , 7 ]. Localization approaches in WSNs can be divided into two types: range-based algorithms [ 8 , 9 ] and range-free algorithms [ 10 , 11 , 12 ]. The range-based (e.g., RSSI-based, Received Signal Strength Indication) localization algorithms estimate the locations of sensor nodes by measuring their distances or angles from the anchor nodes.…”

Section: Introductionmentioning

confidence: 99%

LSRR-LA: An Anisotropy-Tolerant Localization Algorithm Based on Least Square Regularized Regression for Multi-Hop Wireless Sensor Networks

Zhao

Shao

Song

et al. 2018

Sensors

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As is well known, multi-hop range-free localization algorithms demonstrate pretty good performance in isotropic networks in which sensor nodes distribute evenly and densely. However, these algorithms are easily affected by network topology, causing a significant decrease in positioning accuracy. To improve the localization performance in anisotropic networks, this paper presents a multi-hop range-free localization algorithm based on Least Square Regularized Regression (LSRR). By building a mapping relationship between hop counts and real distances, we can regard the process of localization as a regularized regression. Firstly, the proximity information of the given network is measured. Then, a mapping model between the geographical distances and the hop distances is constructed by LSRR. Finally, each sensor node finds its own position via this mapping. The Average Localization Error (ALE) metric is used to evaluate the proposed method in our experiments, and results show that, compared with similar methods, our approach can effectively decrease the effect of anisotropy, thus considerably improving the positioning accuracy.

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Node localization algorithm for wireless sensor networks using compressive sensing theory

Cited by 11 publications

References 14 publications

Design of Optimal Localization Model for Wireless Sensor Network Nodes Using Improved Krill Herd Algorithm

Design of Optimal Localization Model for Wireless Sensor Network Nodes Using Improved Krill Herd Algorithm

Compressive sensing for localisation in wireless sensor networks: an approach for energy and error control

LSRR-LA: An Anisotropy-Tolerant Localization Algorithm Based on Least Square Regularized Regression for Multi-Hop Wireless Sensor Networks

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