Stochastic Multidimensional Scaling

Rajawat, Ketan; Kumar, Sandeep

doi:10.1109/tsipn.2017.2668145

Cited by 11 publications

(9 citation statements)

References 32 publications

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“…As the missing distances are estimated in the previous section, now the surface station uses data analysis methods to estimate the actual location of nodes. Some of the most famous data analysis methods are multidimensional scaling [38], [39], Isomap [56], and principal component analysis [57]. All of these methods are also called dimensionality reduction methods which tries to embed a higher dimensional data into a lower dimensional space.…”

Section: Relative Position Estimationmentioning

confidence: 99%

“…• Finally, analytic findings are verified with extensive simulation results for different system parameters such as the number of nodes, transmission range, and beam scanning angles. Localization performance of the proposed method is also compared with other well-known network localization schemes such as multidimensional scaling (MDS) [37]- [39], and distance vector routing (DV) [40].…”

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confidence: 99%

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Performance Analysis of Connectivity and Localization in Multi-Hop Underwater Optical Wireless Sensor Networks

Saeed

Çelik

Alouini

et al. 2019

IEEE Trans. on Mobile Comput.

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Underwater optical wireless links have limited range and intermittent connectivity due to the hostile aquatic channel impairments and misalignment between the optical transceivers. Therefore, multi-hop communication can expand the communication range, enhance network connectivity, and provide a more precise network localization scheme. In this regard, this paper investigates the connectivity of underwater optical wireless sensor networks (UOWSNs) and its impacts on the network localization performance. Firstly, we model UOWSNs as randomly scaled sector graphs where the connection between sensors is established by point-to-point directed links. Thereafter, the probability of network connectivity is analytically derived as a function of network density, communication range, and optical transmitters' divergence angle. Secondly, the network localization problem is formulated as an unconstrained optimization problem and solved using the conjugate gradient technique. Numerical results show that different network parameters such as the number of nodes, divergence angle, and transmission range significantly influence the probability of a connected network. Furthermore, the performance of the proposed localization technique is compared to well-known network localization schemes and the results show that the localization accuracy of the proposed technique outperforms the literature in terms of network connectivity, ranging error, and number of anchors.

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Section: Relative Position Estimationmentioning

confidence: 99%

mentioning

confidence: 99%

Performance Analysis of Connectivity and Localization in Multi-Hop Underwater Optical Wireless Sensor Networks

Saeed

Çelik

Alouini

et al. 2019

IEEE Trans. on Mobile Comput.

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“…In this paper, we compare our projection algorithm with the other existing projection methods in terms of image semantic segmentation for outdoor scene understanding. Five commonly used projection algorithms are used here, namely, PCA, Kernel PCA (KPCA) [14], MDS [15], LDA [16], and autoencoder [17]. The first three are linear methods, and the latter two are nonlinear methods.…”

Section: B Results Of Rgb-di Image Generatingmentioning

confidence: 99%

RGB-DI Images and Full Convolution Neural Network-Based Outdoor Scene Understanding for Mobile Robots

Qiu

Zhuang

Yan

et al. 2019

IEEE Trans. Instrum. Meas.

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This paper presents a multisensor-based approach to outdoor scene understanding of mobile robots. Since laser scanning points in 3-D space are distributed irregularly and unbalanced, a projection algorithm is proposed to generate RGB, depth, and intensity (RGB-DI) images so that the outdoor environments can be optimally measured with a variable resolution. The 3-D semantic segmentation in RGB-DI cloud points is, therefore, transformed to the semantic segmentation in RGB-DI images. A full convolution neural network (FCN) model with deep layers is designed to perform semantic segmentation of RGB-DI images. According to the exact correspondence between each 3-D point and each pixel in a RGB-DI image, the semantic segmentation results of the RGB-DI images are mapped back to the original point clouds to realize the 3-D scene understanding. The proposed algorithms are tested on different data sets, and the results show that our RGB-DI image and FCN modelbased approach can provide a superior performance for outdoor scene understanding. Moreover, real-world experiments were conducted on our mobile robot platform to show the validity and practicability of the proposed approach.

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“…It is clear from Figure 7 that the efficiency of the localization technique improves with the energy harvested from the aquatic environment. The mean square error performance of the proposed technique is compared with well-known network localization schemes such as multidimensional scaling [ 35 ] and manifold regularization [ 36 ]. Figure 8 shows that the proposed technique outperforms both multidimensional scaling and manifold regularization due to the novel strategy of weighting the multiple observation views for a single network.…”

Section: Numerical Resultsmentioning

confidence: 99%

Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization

Saeed

Çelik

Al-Naffouri

et al. 2017

Sensors

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Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique.

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Stochastic Multidimensional Scaling

Cited by 11 publications

References 32 publications

Performance Analysis of Connectivity and Localization in Multi-Hop Underwater Optical Wireless Sensor Networks

Performance Analysis of Connectivity and Localization in Multi-Hop Underwater Optical Wireless Sensor Networks

RGB-DI Images and Full Convolution Neural Network-Based Outdoor Scene Understanding for Mobile Robots

Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization

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