An Efficient Localization Algorithm in Wireless Sensor Networks

Shen, Shikai; Yang, Bin; Qian, Kaiguo; Jiang, Xiaohong

doi:10.1109/candar.2015.99

Cited by 5 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The coordinates of a known node k are x k , y k , and there are n nodes with known coordinates. The location of this unknown node is calculated as [50]:…”

Section: If σ Imentioning

confidence: 99%

Mobility-aware hierarchical fog computing framework for Industrial Internet of Things (IIoT)

et al. 2022

View full text Add to dashboard Cite

The Industrial Internet of Things (IIoTs) is an emerging area that forms the collaborative environment for devices to share resources. In IIoT, many sensors, actuators, and other devices are used to improve industrial efficiency. As most of the devices are mobile; therefore, the impact of mobility can be seen in terms of low-device utilization. Thus, most of the time, the available resources are underutilized. Therefore, the inception of the fog computing model in IIoT has reduced the communication delay in executing complex tasks. However, it is not feasible to cover the entire region through fog nodes; therefore, fog node selection and placement is still the challenging task. This paper proposes a multi-level hierarchical fog node deployment model for the industrial environment. Moreover, the scheme utilized the IoT devices as a fog node; however, the selection depends on energy, path/location, network properties, storage, and available computing resources. Therefore, the scheme used the location-aware module before engaging the device for task computation. The framework is evaluated in terms of memory, CPU, scalability, and system efficiency; also compared with the existing approach in terms of task acceptance rate. The scheme is compared with xFogSim framework that is capable to handle workload upto 1000 devices. However, the task acceptance ratio is higher in the proposed framework due to its multi-tier model. The workload acceptance ratio is 85% reported with 3000 devices; whereas, in xFogsim the ratio is reduced to approx. 68%. The primary reason for high workload acceptation is that the proposed solution utilizes the unused resources of the user devices for computations.

show abstract

“…The coordinates of a known node k are x k , y k , and there are n nodes with known coordinates. The location of this unknown node is calculated as [50]:…”

Section: If σ Imentioning

confidence: 99%

Mobility-aware hierarchical fog computing framework for Industrial Internet of Things (IIoT)

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In [47] Shikai Shen et al proposed an improved DV-Hop localization algorithm to ensure the accuracy of localization. this localization algorithm first employs distortion function to select the beacon nodes that can estimate average hop distance and then adopt two-dimensional hyperbolic function instead of the classic trilateration/least square method to determine the locations of unknown nodes, which are very close to their actual locations.…”

Section: Related Workmentioning

confidence: 99%

Survey of Localization Techniques based on Mobile Beacon in Wireless Sensor Network

Ubnare¹,

Yadav²,

Chourasiya³

2017

International Journal of Innovative Research in Electrical, Ele

View full text Add to dashboard Cite

Nowadays, Wireless Sensor Networks (WSNs) are most growing research area because of its low cost, infrastructure less, increased capabilities of nodes, real-time and accurate monitoring. It is employed to gather and forward information to the destination. Location of the event or collected information is very crucial for successive operations. This information may be obtained using the global positioning system, but it is not feasible for energy constraints networks. Location of sensor nodes may be obtained through localization techniques. Localization of nodes in a sensor network is a motivating analysis space, and a lot of works are done to this point. It is highly required to design energy aware, economical and scalable localization techniques for WSNs. In this paper, we have done analyze of various localization techniques, and few possible future research directions.

show abstract

“…In [11] Shikai Shen et al proposed an improved DV-Hop localization algorithm to ensure the accuracy of localization. This localization algorithm first employs distortion function to select the beacon nodes that can estimate average hop distance and then adopt two-dimensional hyperbolic function instead of the classic trilateration/least square method to determine the locations of unknown nodes, which are very close to their actual locations.…”

Section: Introductionmentioning

confidence: 99%

Progressive Localization using Mobile Anchor in Wireless Sensor Network

Rathod¹,

Paul²

2017

ijecs

View full text Add to dashboard Cite

Abstract-Wireless sensor network (WSN) is employed to gather and forward information to the destination. It is very crucial to know the location of the event or collected information. This location information may be obtained using GPS or localization technique in wireless sensor networks. Randomly deployed WSN needs a large amount of GPS-enabled sensor nodes for localization, this necessitates progressive approach. However, nodes with sparse connectivity remain unlocalized. In this paper, a progressive mobile anchor based technique is proposed for node localization. Initially, sensor nodes are localized using anchors in the neighborhood, then these localized nodes progressively localized remaining nodes using multilateration. Mobile anchor node moves randomly in field and broadcast position information. It localized nodes with sparse connectivity. Simulation results show that proposed approach localize all sensor nodes with good accuracy.

show abstract

An Efficient Localization Algorithm in Wireless Sensor Networks

Cited by 5 publications

References 10 publications

Mobility-aware hierarchical fog computing framework for Industrial Internet of Things (IIoT)

Mobility-aware hierarchical fog computing framework for Industrial Internet of Things (IIoT)

Survey of Localization Techniques based on Mobile Beacon in Wireless Sensor Network

Progressive Localization using Mobile Anchor in Wireless Sensor Network

Contact Info

Product

Resources

About