Cooperative Localization of Mobile Networks Via Velocity-Assisted Multidimensional Scaling

Kumar, Sandeep; Kumar, Raju Suresh; Rajawat, Ketan

doi:10.1109/tsp.2015.2507548

Cited by 32 publications

(27 citation statements)

References 27 publications

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“…Intuitively, Proposition 1 states that the trajectory of the proposed stochastic algorithm in (13) stays close to that of the averaged algorithm in (16). Further, the stochastic "oscillations" of (13) are small if µ is also small. However, choosing too small a value of µ, which is also the step-size in (16), will generally result in a slower convergence rate for any such iterative algorithm.…”

Section: Iv-a)mentioning

confidence: 83%

“…Such a condition is required to ensure the numerical stability of the Laplacian system of equations that must be December 22, 2016 DRAFT solved at every iteration [cf. (10), (13)]. Specifically, it is shown in Appendix that (A2) implies the following result…”

Section: ) Assumptionsmentioning

confidence: 89%

“…Wireless network localization is challenging because the pairwise distance measurements are noisy, time-varying due to mobility, fading, and synchronization errors, and often partially missing, due to the limited range of the sensors. Further, the limited battery life and resource constraints at the nodes impose restrictions on the communication and computational load that the network can tolerate [13], [14].…”

Section: A Multi-agent Network Localizationmentioning

confidence: 99%

“…Since the stress function is a weighted sum of squared fitting errors, it allows for the possibility of missing and noisy dissimilarities. Consequently variants of the stress minimization problem have been developed for robust MDS [11], visualization of timevarying data [12], and cooperative localization of static [10], [13]- [15] and mobile networks [13].…”

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confidence: 99%

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Stochastic Multidimensional Scaling

Rajawat

Kumar

2017

IEEE Trans. on Signal and Inf. Process. over Networks

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Multidimensional scaling (MDS) is a popular dimensionality reduction techniques that has been widely used for network visualization and cooperative localization. However, the traditional stress minimization formulation of MDS necessitates the use of batch optimization algorithms that are not scalable to large-sized problems. This paper considers an alternative stochastic stress minimization framework that is amenable to incremental and distributed solutions. A novel linear-complexity stochastic optimization algorithm is proposed that is provably convergent and simple to implement. The applicability of the proposed algorithm to localization and visualization tasks is also expounded. Extensive tests on synthetic and real datasets demonstrate the efficacy of the proposed algorithm.

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Section: Iv-a)mentioning

confidence: 83%

Section: ) Assumptionsmentioning

confidence: 89%

Section: A Multi-agent Network Localizationmentioning

confidence: 99%

mentioning

confidence: 99%

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Stochastic Multidimensional Scaling

Rajawat

Kumar

2017

IEEE Trans. on Signal and Inf. Process. over Networks

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“…For example, in [193] the authors used extended Kalman filter and unscented Kalman filter with MDS to track mobile sensors. A low complexity majorization function with MDS is used in [194], [195] to track mobile sensor nodes. Distributed MDS based localization algorithm is proposed in [25] with noisy range measurements, where the authors assume that the distances are corrupted with independent Gaussian random noise.…”

Section: Distributed Mds Based Localizationmentioning

confidence: 99%

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

Saeed

Nam

Al-Naffouri

et al. 2019

IEEE Commun. Surv. Tutorials

147

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Current and future wireless applications strongly rely on precise real-time localization. A number of applications such as smart cities, Internet of Things (IoT), medical services, automotive industry, underwater exploration, public safety, and military systems require reliable and accurate localization techniques. Generally, the most popular localization/ positioning system is the Global Positioning System (GPS). GPS works well for outdoor environments but fails in indoor and harsh environments. Therefore, a number of other wireless local localization techniques are developed based on terrestrial wireless networks, wireless sensor networks (WSNs) and wireless local area networks (WLANs). Also, there exist localization techniques which fuse two or more technologies to find out the location of the user, also called signal of opportunity based localization. Most of the localization techniques require ranging measurements such as time of arrival (ToA), time difference of arrival (TDoA), direction of arrival (DoA) and received signal strength (RSS). There are also range-free localization techniques which consider the proximity information and do not require the actual ranging measurements. Dimensionality reduction techniques are famous among the range free localization schemes. Multidimensional scaling (MDS) is one of the dimensionality reduction technique which has been used extensively in the recent past for wireless networks localization. In this paper, a comprehensive survey is presented for MDS and MDS based localization techniques in WSNs, Internet of Things (IoT), cognitive radio networks, and 5G networks. 2 MDS is much popular among all these techniques because of its simplicity and many application areas. MDS analysis finds the spatial map for objects given that the similarity or dissimilarity information between the objects is available [16].In the recent past, MDS is widely used for localization and mapping of wireless sensor networks (WSNs) and the internet of things (IoT). In [17] a proximity information based sensor network localization is proposed, where the main idea is to construct a local configuration of sensor nodes by using classical MDS (CMDS). The MDS based localization algorithms in [17] and [18] are centralized with higher computational complexity [7]. Semi-centralized (or clustered) MDS techniques are developed to compute local coordinates of nodes, which then are refined to find the final position of the nodes [19], [20]. In [21], [22] and [23] the authors proposed manifold learning to estimate the sensor nodes position in wireless sensor networks. In [24] the authors proposed Nystrom approximation for the proximity information matrix in MDS to reduce its size for better localization accuracy in sensor networks. Distributed MDS based localization algorithm is proposed in [25] with noisy range measurements, where the authors assume that the distances are corrupted with independent Gaussian random noise. MDS methods with different refinement schemes have also been proposed in the literature t...

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