A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> Utilizing user location as a key determinant of information requirement needs. <s> BIB001 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> Fundamentals of MDS.- The Four Purposes of Multidimensional Scaling.- Constructing MDS Representations.- MDS Models and Measures of Fit.- Three Applications of MDS.- MDS and Facet Theory.- How to Obtain Proximities.- MDS Models and Solving MDS Problems.- Matrix Algebra for MDS.- A Majorization Algorithm for Solving MDS.- Metric and Nonmetric MDS.- Confirmatory MDS.- MDS Fit Measures, Their Relations, and Some Algorithms.- Classical Scaling.- Special Solutions, Degeneracies, and Local Minima.- Unfolding.- Unfolding.- Avoiding Trivial Solutions in Unfolding.- Special Unfolding Models.- MDS Geometry as a Substantive Model.- MDS as a Psychological Model.- Scalar Products and Euclidean Distances.- Euclidean Embeddings.- MDS and Related Methods.- Procrustes Procedures.- Three-Way Procrustean Models.- Three-Way MDS Models.- Modeling Asymmetric Data.- Methods Related to MDS. <s> BIB002 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. We present an algorithm that uses connectivity information who is within communications range of whom to derive the locations of the nodes in the network. The method can take advantage of additional information, such as estimated distances between neighbors or known positions for certain anchor nodes, if it is available. The algorithm is based on multidimensional scaling, a data analysis technique that takes O(n3) time for a network of n nodes. Through simulation studies, we demonstrate that the algorithm is more robust to measurement error than previous proposals, especially when nodes are positioned relatively uniformly throughout the plane. Furthermore, it can achieve comparable results using many fewer anchor nodes than previous methods, and even yields relative coordinates when no anchor nodes are available. <s> BIB003 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS. <s> BIB004 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> The performance of most dynamic load balancing schemes, proposed to solve the hot spot problem in cellular systems, depends on the user distribution within a cell. In this paper, we first study the impact of user distribution on the call blocking probability performance that can be achieved by two key dynamic load balancing schemes (i.e., channel borrowing without locking (CBWL) and integrated cellular and ad hoc relay (iCAR) system (H. Jiang and S.S Rappaport, 1994)-(H. Wu et al., 2001)). We show that the achievable call blocking probability performance is location-dependent, and iCAR and CBWL schemes might not be able to achieve 2% call blocking probability for all user distributions, unless major modifications are made to CBWL or iCAR to overcome local congestion problems. Therefore, in this paper, we propose to use a joint dynamic load balancing scheme that combines the features of CBWL and iCAR schemes. Our objective is to guarantee a low call blocking probability in congested cells irrespective of user location. <s> BIB005 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> Given a set of pairwise distance estimates between nodes, it is often of interest to generate a map of node locations. This is an old problem that has attracted renewed interest in the signal processing community, due to the recent emergence of wireless sensor networks and ad-hoc networks. Sensor maps are useful for estimating the spatial distribution of measured phenomena, as well as for routing purposes. Both centralized and decentralized solutions have been developed, along with ways to cope with missing data, accounting for the reliability of individual measurements, etc. We revisit the basic version of the problem, and propose a two-stage algorithm that combines algebraic initialization and gradient descent. In particular, we borrow an algebraic solution from the database literature and adapt it to the sensor network context, using a specific choice of anchor/pivot nodes. The resulting estimates are fed to gradient descent iteration. The overall algorithm offers better performance at lower complexity than existing centralized full-connectivity solutions. Also, its performance is relatively close to the corresponding Cramer-Rao bound, especially for small values of range error variance. <s> BIB006 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> There are various applications in wireless sensor networks which require knowing the relative or actual position of the sensor nodes. Recently, there have been different localization algorithms proposed in the literature. The algorithms based on classical Multidimensional Scaling (MDS) only require 3 or 4 anchor nodes and can provide higher accuracy than some other schemes. In this paper, we propose and analyze another type of MDS (called ordinal MDS) for localization in wireless sensor networks. Ordinal MDS differs from classical MDS by that it only requires a monotonicity constraint between the shortest path distance and the Euclidean distance for each pair of nodes. We conduct simulation studies under square and C-shaped topologies with different connectivity levels and number of anchors. Results show that ordinal MDS provides a lower position estimation error than classical MDS. <s> BIB007 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> Advances in micro-electro-mechanical systems have triggered an enormous interest in wireless sensor networks (WSN). WSN are formed by large numbers of densely deployed nodes enabled with sensing and actuating capabilities. These nodes have very limited processing and memory capabilities, limited energy resources and it is envisioned that they will be mass produced, to reduce costs. <s> BIB008 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> In many applications of wireless sensor networks, location is very important information. Conventional location information comes from manual setting or GPS device. However, manual location setting requires huge cost of human time, and GPS location setting requires expensive device cost and large device size. Furthermore, GPS can not operate in indoor environment. Both approaches are not applicable to localization task of wireless sensor networks. In this paper, an accurate and efficient localization algorithm based on multidimensional scaling (MDS) is proposed in hierarchical network environment. Through localized computation of multidimensional scaling within a cluster, the computation overhead can be distributed to each cluster. On the other hand, MDS-based localization approach requires the estimation of multihop distance. Through restricting estimation of multihop distances within a cluster, the proposed localization algorithm achieves better accuracy and can operate in non-convex network environment. Experimental results reveal that the proposed HMDS localization algorithm outperforms MDSMAP algorithm in terms of accuracy. <s> BIB009 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> We propose an accurate, distributed localization method that uses the connectivity measure to localize nodes in a wireless sensor network. The proposed method is based on a self-organizing isometric embedding algorithm that adaptively emphasizes the most accurate range of measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors and updates its estimate of position by minimizing a local cost function and then passes this update to the neighboring sensors. Simulations demonstrate that the proposed method is more robust to measurement error than previous methods and it can achieve comparable results using much fewer anchor nodes than previous methods. <s> BIB010 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> A wide range of applications used on wireless sensor networks requires the location information of sensor nodes. Some conventional localization techniques employ GPS or other location-tracking devices installed on sensor nodes. On the other hand, other methods provide location information by taking into account node connectivity only. One example of such methods is a multidimensional scaling-MAP (MDS-MAP), which provides the most accurate location information to date. MDS-MAP, however, has a computational overhead of O(n^3) in a network of n nodes and also it intrinsically generates significant localization accuracy error in an environment with holes. This paper proposes a cluster-based MDS scheme for range-free localization, CMDS for short, which addresses the shortcomings of MDS-MAP and yields higher accuracy in all environments. Simulation results demonstrate that in a sensor network environment with holes, the proposed scheme gives up to 23% improvement in localization accuracy over hierarchical MDS (HMDS), which is an enhanced version of the conventional MDS-MAP. <s> BIB011 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> Reconfigurable manufacturing systems (RMSs) has emerged as an advanced system for applying Enterprise Systems (ES) in manufacturing enterprises to dynamically adapt to the changing market. Although it has been recognized that human-machine interaction plays a significant role in configuration design, especially for reconfigurable machines tools (RMTs), for the core of RMS, there are no platforms available for the harmonious interaction between human and computer, mainly because of the limitation of traditional artificial intelligence frame. In this paper, we present a human-machine design methodology for the configuration of RMTs based on imaginal thinking, a thinking style described as forming and comparison of images, which is radically different from traditional logical and intuitive thinking. The knowledge and experience that are highly relevant to mechanical design and the related information in logical deduction are organized as high dimensional images. By imitating human imaginal thinking, computer employs Lie subgroup or submanifold and Quotient kinematics to generate all possible configurations in the form of images, while humans make judgments and complete the optimization process through the imaginal human-machine interface. Both human's decision-making ability based on knowledge and experience and computer's high-speed logical computation are exploited to a full extent. This novel design methodology has illustrated its effectiveness by an example concerning the design of a pipe-cutting RMT. <s> BIB012 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> This study deals with the localisation of all nodes in a network, also called as network localisation, based on pairwise distance measurements. The case of a fully connected network is considered, where ‘fully connected’ refers to that within the whole network every pair of nodes directly connect to each other, thus their pairwise distance can be measured and available. For the localisation of such a network, the multi-dimensional scaling (MDS) algorithm can provide a relative localisation solution, but only a coarse solution when there are measurement errors. To bridge the gap in the localisation performance between the MDS-based and optimum network localisation, the authors propose an efficient subsequent refinement, that is, the iterative least square (LS)/weighted least square (WLS) refinement for the widely existing independent zero-mean Gaussian measurement errors. Analysis and simulation study show that with sufficiently small measurement errors the proposed improved network localisation scheme can achieve, in very limited iterations, the LS/WLS solution, which exhibits the localisation performance the same as the Cramer–Rao lower bound. The authors also extend the proposed refinement to the absolute localisation case with sufficient position-known anchors that are fully and directly connected to all sensors of the network. <s> BIB013 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> We offer a redesigned form of the classical multidimensional scaling (C-MDS) algorithm suitable to handle the localization of multiple sources under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. To do so we propose to modify the kernel matrix used in the MDS algorithm to allow for both distance and angle information to be processed algebraically (without iteration) and simultaneously. In so doing we also show that the new formulation overcomes two well known limitations of the C-MDS approach, namely the propagation error problem and the possibility to weight the dissimilarities used as measurement information, including, for the case of binary weights, the data erasure problem. Due to the increased size of the proposed edge kernel matrix KE used in the algorithm, the Nystrom approximation is applied to reduce the overall computational complexity to few matrix multiplications. Range only scenarios are also dealt with by approximating the matrix KE. Simulations in range-angle as well as range-only scenarios demonstrate the superiority of our solution under both LOS and NLOS conditions versus semidefinite programming (SDP) formulations of the problem specifically designed to exploit the heterogeneity of the information available. <s> BIB014 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> This paper presents the state-of-the-art of the leading indoor positioning technologies and systems. The positioning technology is classified into the active positioning system and the passive positioning system, which are presented respectively in detail, including wireless local area network, radio frequency identification, Bluetooth, inertial navigation, and magnetic field positioning. Additionally, the advantages and disadvantages of various indoor positioning technologies and systems are analyzed, as well as the positioning accuracy, applicability, and working principle. Particularly, in order to better understand them, the key performance parameters of the mentioned technologies and systems are finally compared using a table. <s> BIB015 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> Recent advancements in micro electro mechanical system and wireless communication have made it possible to use wireless sensor nodes for many real world applications. Wireless sensor nodes are widely been used now a days in many tracking and monitoring problems such as vehicle tracking, environmental and health monitoring. It is one of the most challenging problems to find the geographic locations of nodes given that proximity information only between the nodes. There are large number of localization algorithms, each having different approach to find the final location. Multidimensional Scaling (MDS) is one of that technique which works in more robust fashion for noise sparse networks, even with less number of anchor nodes. The proposed work addresses the problem of location discovery of nodes based on MDS and Levenberg-Maurquardt (LM) method. In this paper we used MDS to find the initial estimated position of nodes and proposed Levenberg-Maurquardt as a refinement algorithm for final position. Our simulation results show that the proposed scheme is very competitive compared to other common schemes. <s> BIB016 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> In cognitive radio networks, secondary users (SUs) can share spectrum with primary users (PUs) under the condition that no interference is caused to the PUs. To evaluate the interference imposed to the PUs, the cognitive systems discussed in the literature usually assume that the channel state information (CSI) of the link from a secondary transmitter to a primary receiver (interference link) is known at the secondary transmitter. However, this assumption may often be impractical in cognitive radio systems, since the PUs need to be oblivious to the presence of the SUs. The authors first discuss PU localisation and then introduce an uplink resource allocation algorithm for orthogonal frequency division multiple access-based cognitive radio systems, where relative location information between primary and SUs is used instead of CSI of the interference link to estimate the interference. Numerical and simulation results show that it is indeed effective to use location information as a part of resource allocation and thus a near-optimal capacity is achieved. <s> BIB017 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> Localization of primary users (PUs) and secondary users (SUs) is one of the essential features of cognitive radio networks (CRNs). Given that there is no communication between PUs and SUs, localization of the whole network is a challenging task. In this paper, we propose a two-stage localization algorithm that combines multidimensional scaling (MDS) and Procrustes analysis for a CRN with proximity information. In the proposed algorithm, a hybrid-connectivity-and-estimated-distance-based strategy is introduced to get maximum benefit from the information available in the network. Simulations are included to compare the proposed algorithm with weighted centroid localization (WCL) in terms of the root-mean-square-error (RMSE) performance, as well as the Cramer–Rao lower bound (CRLB) for CRN localization. It is proved that the proposed algorithm outperforms the WCL solutions for the CRN localization problem. <s> BIB018 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> Abstract Recent developments in micro electro mechanical systems (MEMS) technology and wireless communication have propelled the growing applications of wireless sensor networks (WSNs). Wireless sensor network is comprised of large number of small and cheap devices known as sensors. One of the important functions of sensor network is collection and forwarding of data. In most of the applications, it is of much interest to find out the location of the data. This type of information can be obtained by use of localization techniques. So node localization is very crucial to find out the position of node with the help of localization algorithms. Hence, node localization becomes one of the fundamental challenges in WSNs. We make the rigorous reviews on different schemes of localization in sensor networks. On the basis of range measurements, the localization schemes can be broadly classified in two categories such as: range based and range free schemes. The cost and hardware limitation on sensing node preclude the use of range based localization schemes. In most of the sensor network application coarse accuracy is sufficient so range free localization schemes are considered as a substitute to range based schemes. In this paper, the detailed study has been carried out to understand and select the best range free localization algorithm for WSNs. At the end some issues are discussed for future research in the area of localization techniques for WSNs. <s> BIB019 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> This paper considers the noisy range-only network localization problem in which measurements of relative distances between agents are used to estimate their positions in networked systems. When distance information is noisy, existence and uniqueness of location solution are usually not guaranteed. It is well known that in presence of distance measurement noise, a node may have discontinuous deformations (e.g., flip ambiguities and discontinuous flex ambiguities). Thus, there are two issues that we consider in the noisy localization problem. The first one is the location estimate error propagated from distance measurement noise. We compare two kinds of analytical location error computation methods by assuming that each distance is corrupted with independent Gaussian random noise. These analytical results help us to understand effects of the measurement noises on the position estimation accuracy. After that, based on multidimensional scaling theory, we propose a distributed localization algorithm to solve the noisy range network localization problem. Our approach is robust to distance measurement noise, and it can be implemented in any random case without considering the network setup constraints. Moreover, a refined version of distributed noisy range localization method is developed, which achieves a good tradeoff between computational effort and global convergence especially in large-scale networks. <s> BIB020 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> Localization is one of the most important research subjects in terms of the wireless sensor networks (WSNs), because most of the information measured and distributed by the sensors are useful when sensors locations are well known. In this paper, a range free localization algorithm for sensor positioning is proposed. It is based on sensor distributed network model, coverage radius, hop count between each sensor and anchor, and improvement in the minimum mean square error, which computes coefficients for distance estimation between sensors and anchors. In the proposed method, the best coefficient for each hop count is computed with offline processing and Monte Carlo method. Then, these coefficients are stored in each sensor database and they are used for localization in the practical environment. Unlike some existent positioning methods, in this algorithm, it is not required to compute distance estimation coefficient by sensors. In the proposed method, it is assumed that all sensors use the omnidirectional antenna for their usual information transmission. High precision in geographical coordinate determining, less traffic load, and especially good performance in the homogeneous and non-homogeneous environment are the most important features of this algorithm. Simulation results show that the proposed algorithm has a good position determination and reduced traffic load for WSNs, as compared with some existent positioning schemes. Indeed, the proposed method improves localization precision and reduces traffic load simultaneously. <s> BIB021 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> Localization in wireless sensor networks (WSNs) is one of the primary functions of the intelligent Internet of Things (IoT) that offers automatically discoverable services, while the localization accuracy is a key issue to evaluate the quality of those services. In this paper, we develop a framework to solve the Euclidean distance matrix completion problem, which is an important technical problem for distance-based localization in WSNs. The sensor network localization problem is described as a low-rank dimensional Euclidean distance completion problem with known nodes. The task is to find the sensor locations through recovery of missing entries of a squared distance matrix when the dimension of the data is small compared to the number of data points. We solve a relaxation optimization problem using a modification of Newton’s method, where the cost function depends on the squared distance matrix. The solution obtained in our scheme achieves a lower complexity and can perform better if we use it as an initial guess for an interactive local search of other higher precision localization scheme. Simulation results show the effectiveness of our approach. <s> BIB022 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> In this paper, we propose a matrix completion algorithm for Internet of Things (IoT) localization. In the proposed algorithm, we recast Euclidean distance matrix completion problem as an unconstrained optimization in smooth Riemannian manifold and then propose a nonlinear conjugate gradient method on this manifold to reconstruct Euclidean distance matrix. The empirical results show that the proposed algorithm is effective and also outperforms state-of-the-art matrix completion algorithms both in noise and noiseless scenarios. <s> BIB023 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> Indoor positioning systems (IPS) use sensors and communication technologies to locate objects in indoor environments. IPS are attracting scientific and enterprise interest because there is a big market opportunity for applying these technologies. There are many previous surveys on indoor positioning systems; however, most of them lack a solid classification scheme that would structurally map a wide field such as IPS, or omit several key technologies or have a limited perspective; finally, surveys rapidly become obsolete in an area as dynamic as IPS. The goal of this paper is to provide a technological perspective of indoor positioning systems, comprising a wide range of technologies and approaches. Further, we classify the existing approaches in a structure in order to guide the review and discussion of the different approaches. Finally, we present a comparison of indoor positioning approaches and present the evolution and trends that we foresee. <s> BIB024 </s> A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques <s>  <s> Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time of flight (RTOF), and received signal strength (RSS); based on technologies, such as WiFi, radio frequency identification device (RFID), ultra wideband (UWB), Bluetooth, and systems that have been proposed in the literature. This paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability, and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization. <s> BIB025
of location information for network planning BIB001 , resource allocation BIB017 , load balancing BIB005 , spatial spectrum sensing BIB018 , and network adaptation  , etc. Global positioning systems are also known as global navigation and satellite systems (GNSS) allow each user to figure out its location globally. GNSS consists of different positioning systems from different countries such as the global positioning system (GPS), GALILEO, "Globalnaya navigatsionnaya sputnikovaya sistema" (GLONASS) and BeiDou  . GPS and GNSS work well for outdoor environments, but it fails to localize a user in an indoor or harsh environment. In comparison to the outdoor environment, the indoor environment is more challenging and complex. The various obstacles such as human beings, walls, equipment's, ceilings, etc., influence the propagation of signals, thus leads to multi-path propagation error. In addition to that, interference is also added to the propagating signal by noise sources from other wireless networks. Considering these issues in the indoor environment, the development of indoor positioning systems is challenging for future wireless communication systems. A number of survey articles are presented on the design and development of indoor positioning systems such as BIB015 , BIB024 , and BIB025 . Indoor positioning systems have been developed by different research centers, companies, and universities based on various wireless communication technologies operating on different frequencies such as acoustic waves, radio frequency (RF), ultra-wideband, infrared, and visible light. All of the above mentioned indoor positioning systems are based on a specific ranging technique. Since the cost and hardware limitation of sensors often prevent the localization systems from using range-based techniques, range-free localization techniques are developed to substitute the range-based techniques. Range-free localization techniques are dependent on the connectivity information which is a much cheaper solution than the range-based techniques because these techniques do not require extra hardware to compute the actual range and rely only on the proximity information BIB019 , BIB021 . Range-free schemes are performed by using the constraint optimization, geometric interpretation, and area formation techniques BIB008 . Multidimensional scaling (MDS) is one of the most common network localization techniques which can work for both range-free and rangebased schemes. MDS is one of the dimensionality reduction techniques which converts multidimensional data into lower dimensional space while keeping the essential information. The main benefit of using MDS is to get a graphical display for the given data, such that it is much easier to understand. There exists other dimensionality reduction techniques like principal component analysis (PCA), factor analysis and Isomap but 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 BIB002 . In the recent past, MDS is widely used for localization and mapping of wireless sensor networks (WSNs) and the Internet of Things (IoT). In BIB003 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 BIB003 and BIB007 are centralized with higher computational complexity BIB018 . 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 BIB009 , BIB011 . In BIB004 , BIB010 and BIB012 the authors proposed manifold learning to estimate the sensor nodes position in wireless sensor networks. In BIB014 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 BIB020 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 to get better localization accuracy for the sensor nodes in WSNs BIB006 BIB013 BIB016 . More recently a Euclidean distance matrix completion method is proposed for MDS in BIB022 , BIB023 to find the map of an IoT network. Although the literature on MDS based network localization techniques is not rich, it can be well adapted for modern wireless communication systems such as Internet of Things IoT, 5G networks, and underwater wireless communication networks. MDS based network localization can provide efficient data fusion mechanisms for IoT networks. Similarly, MDS based location awareness for 5G networks will provide numerous applications such as radio resource management, routing, and defining radio maps. Moreover, MDS based localization for softwaredefined networks will enable a centralized map of the whole network including the different entities of the system which can be helpful for various networking issues. In short, all of the modern wireless communication networks require accurate network localization schemes to provide different applications which include but not limited to data tagging, location-aware routing, environment monitoring, and navigation. Therefore, MDS is one of the famous network localization technique which can be applied to these networks to provide such applications.