Range-free localization approach for M2M communication system using mobile anchor nodes

Karim, Lutful; Mahmoud, Qusay H.; Anpalagan, Alagan; Salti, Tarek El

doi:10.1016/j.jnca.2014.07.034

Cited by 16 publications

(18 citation statements)

References 22 publications

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“…For each result in the figures below,

Q = 10, 000

runs were done. The performance of proposed GSAP is compared with that of existing methods CL [7], WCL [8], LLS [10], subspace [11], and the CRLB provided in Equation (21). For a termination criterion of the localization algorithm, we have used

δ = 10^{- 4}

.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In CL [7], BM selects all those anchors that are one-hop neighbors of that BM. In [21], in order to increase the accuracy, a convex-hull anchor selection method is proposed by considering the geometry of anchors, where only those anchors that have the greatest distance from each other are selected.…”

Section: Greedy Successive Anchorization Process (Gsap)mentioning

confidence: 99%

“…Localization algorithms that are used to determine the unknown location of devices are computationally complicated in radar and sonar systems [5,6]. However, MTC devices are usually small, and, hence, the localization algorithms should be simple and distributed [7,8,9,10,11]. …”

Section: Introductionmentioning

confidence: 99%

“…In [7], a distributed and range-free centroid localization (CL) method is proposed on the assumption that every blind machine (BM that needs to determine its location) should be in the neighbor of three or more AMs. The CL is extremely simple but a little bit inaccurate and requires uniformly distributed AMs near BM.…”

Section: Introductionmentioning

confidence: 99%

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Greedy Successive Anchorization for Localizing Machine Type Communication Devices

Haq

Kim

2016

Sensors

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Localization of machine type communication (MTC) devices is essential for various types of location-based applications. In this paper, we investigate a distributed localization problem in noisy networks, where an estimated position of blind MTC machines (BMs) is obtained by using noisy measurements of distance between BM and anchor machines (AMs). We allow positioned BMs also to work as anchors that are referred to as virtual AMs (VAMs) in this paper. VAMs usually have greater position errors than (original) AMs, and, if used as anchors, the error propagates through the whole network. However, VAMs are necessary, especially when many BMs are distributed in a large area with an insufficient number of AMs. To overcome the error propagation, we propose a greedy successive anchorization process (GSAP). A round of GSAP consists of consecutive two steps. In the first step, a greedy selection of anchors among AMs and VAMs is done by which GSAP considers only those three anchors that possibly pertain to the localization accuracy. In the second step, each BM that can select three anchors in its neighbor determines its location with a proposed distributed localization algorithm. Iterative rounds of GSAP terminate when every BM in the network finds its location. To examine the performance of GSAP, a root mean square error (RMSE) metric is used and the corresponding Cramér–Rao lower bound (CRLB) is provided. By numerical investigation, RMSE performance of GSAP is shown to be better than existing localization methods with and without an anchor selection method and mostly close to the CRLB.

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“…For each result in the figures below,

Q = 10, 000

δ = 10^{- 4}

.…”

Section: Numerical Resultsmentioning

confidence: 99%

Section: Greedy Successive Anchorization Process (Gsap)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Greedy Successive Anchorization for Localizing Machine Type Communication Devices

Haq

Kim

2016

Sensors

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“…In order to reduce the number of static anchor nodes, reduce the operation cost of the whole network, and improve the localization effect, the localization algorithm based on dynamic anchor nodes has obvious advantages [8]. Karim et al [9] proposed a range-free, energy-efficient, localization technique based on mobile anchor nodes for the large-scale machine-to-machine environment. Zhao et al [10] proposed an RSSI-based localization algorithm, which used the RSSI values received by a sensor node from mobile anchor node to estimate the position of the sensor node.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Localization Algorithm of WSN Nodes Based on RSSI-TOA and Single Mobile Anchor Node

Zhang

Yang

Zhang

et al. 2019

Journal of Electrical and Computer Engineering

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Aimed at the shortcomings of low localization accuracy of the fixed multianchor method, a three-dimensional localization algorithm for wireless sensor network nodes is proposed in this paper, which combines received signal strength indicator (RSSI) and time of arrival (TOA) ranging information and single mobile anchor node. A mobile anchor node was introduced in the proposed three-dimensional localization algorithm for wireless sensor networks firstly, and the mobile anchor node moves according to the Gauss-Markov three-dimensional mobility model. en, based on the idea of using RSSI ranging in the near end and TOA ranging in the far end, a ranging method combining RSSI and TOA ranging information is proposed to obtain the precise distance between the anchor node and the unknown node. Finally, the maximum-likelihood estimation method is used to estimate the position of unknown nodes based on the obtained ranging values. e MATLAB simulation results show that the proposed algorithm had a higher localization accuracy and lower localization energy consumption compared with the traditional RSSI localization method or TOA localization method.

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Model for autonomous agents in machine‐to‐machine navigation networks

Ali

Shah

Aslam

2017

Int J Communication

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Machine-to-machine (M2M) is an evolving architecture and tends to provide enormous services through the swarm presence of the networked devices. Localization is one of those services. Previous localization techniques require complex computation that is not suitable and affordable in such architecture. Moreover, integrating intelligent multiagents on these ubiquitous devices makes the network more independent and reactive requiring for a less complex localization model. This paper reviews the present localization techniques and discusses their infeasibility for M2M communication while proposing a mathematical model that is derived from Anderson model for the distributed structure of machine-type-communication network involving autonomous agents. This paper has made an attempt to use the property of Anderson model that structures the distributed objects. This paper also classifies autonomous agents according to their functionalities in a navigational network. Recently, Anderson model have been customized for implication of optical communication; in this paper, the proposed mathematical model involves intelligent agents for localization that aim to reduce complexity of positioning computations for nodes having restricted computational resources and battery life, which are the main characteristics of M2M communication.

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Range-free localization approach for M2M communication system using mobile anchor nodes

Cited by 16 publications

References 22 publications

Greedy Successive Anchorization for Localizing Machine Type Communication Devices

Greedy Successive Anchorization for Localizing Machine Type Communication Devices

Three-Dimensional Localization Algorithm of WSN Nodes Based on RSSI-TOA and Single Mobile Anchor Node

Model for autonomous agents in machine‐to‐machine navigation networks

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