MDS-MAP Algorithm Research Articles

Interference Sources Localization and Communication Relationship Inference With Cognitive Radio IoT Networks

With the widespread application of Internet of things (IoT), the interference problem becomes more and more serious, which results in not only the poor network performance but also the increased energy consumption of IoT nodes. Therefore, in this paper, we investigate the problem of how to locate the interference sources and infer their communication relationships in the cognitive radio IoT (CR-IoT) deployment scenario. First, we utilize MDS-MAP(P) algorithm with dynamic power control to realize cooperative self-localization of the CR-IoT nodes, which is more energy-efficient than all the IoT nodes equipped with global positioning system (GPS) receivers. Then, we propose a non-cooperative localization method to determine the inference sources with the angle of arrivals (AoAs) measured by the CR-IoT nodes. Finally, the communication relationship between interference sources can be inferred based on the association rule of signals. The network simulation results validate that the proposed methods can locate the interference sources accurately with low energy consumption and correctly infer their communication relationships, which is helpful for the interrupted CR-IoT nodes to take a specific opportunistic transmission policy to reduce their energy consumption.

An Optimal Localization Algorithm of Wireless Sensor Networks

To overcome the low accuracy and high energy consumption of positioning algorithm in wireless sensor network, we proposed a optimization positioning based on algorithm neighborhood model. Based on the characteristics of the neighborhood model, the algorithm selects the best beacon node and calculates the proximity distance to transmit the distance information to the base station. The base station uses the MDS-MAP algorithm to determine the location of the unknown node. The simulation was conducted on NS-2 platform. The results show that the performance of the proposed algorithm was better than traditional optimization algorithms. Significant enhancement is obtained with the proposed algorithm in terms of node distance estimation error and position error.

Development of MDS-based localization algorithm in wireless sensors networks:A survey

Wireless sensor networks (WSN), as a new information acquisition and processing technology in target tracking, intrusion detection, positioning and other related fields, has broad application prospects. Multidimensional Scaling, which can be run in both range-based and range-free conditions, and pr oved to achieve high accuracy in range-based cases, is attracting a lot of researches. In this paper, we review the development of WSNs and the location based on MDS-MAP algorithm. MDS-MAP is an inherently centralized algorithm and is therefore of limited utility in many applications. We summarize the improvements that have been made over the past 20 years, and make a comparison of various methods.

An Enhanced Distributed Localization Algorithm Based on MDS-MAP in Wireless Sensor Networks

The classical MDS-MAP algorithm is a centralized algorithm, with an increase in nodes, the algorithm attains a high degree of complexity. In order to solve the shortcomings of the positioning accuracy and the computational complexity of the matrix in the classical MDS-MAP algorithm, an enhanced distributed MDS-MAP localization algorithm was designed and realized (EMDS-MAP(D)). The EMDS-MAP(D) algorithm does not need auxiliary hardware facilities, and can be used for the local computation of nodes, thereby reducing the amount of computation and communication .It is suitable for a shielding environment. The algorithm calculates the coordinates of relative nodes without the anchor node, only transformation absolute coordinates need a Global Positioning System (GPS) to locate a certain amount of coordinates (usually less than 10) and the number of the positioning coordinates does not depend on the size of the network. Theoretical analysis and simulation experimental results show that EMDS-MAP(D) can realize distributed computing and improve the positioning accuracy of the node.

A novel heuristic algorithm for node localization in anisotropic wireless sensor networks with holes

The node localization is a crucial technology that affects practicality, accuracy and effectiveness of the wireless sensor networks (WSNs). Sensor nodes are often deployed non-uniformly in anisotropic WSNs with holes in various applications such as monitoring area terrain. The existence of holes will invariably affect the Euclidean distances between nodes and result in low accuracy of node localization. In this paper, a Heuristic Multidimensional Scaling (HMDS) algorithm is proposed to improve accuracy of node localization in anisotropic WSNs with holes. By exploring the virtual node and constructing the shortest paths between nodes, the Euclidean distances between nodes are obtained via employing the heuristic approach such that they can be used to calculate more accurate locations of the nodes. The HMDS algorithm greatly reduces the communication complexity and computational complexity compared with the MDS-MAP algorithm. Simulation results demonstrate that the HMDS algorithm requires fewer anchors to obtain the node locations. The HMDS algorithm is suitable for four different topologies, including the semi-C-shape topology, the O-shape topology, the multiple O-shape topology and the concave-shape topology and is exceedingly accurate and efficient comparing with state-of-the-art methods in anisotropic WSNs with holes.

Distributed locating algorithm MDS-MAP (LF) based on low-frequency signal

The positioning error of distributed MDS-MAP algorithms comes from two aspects: the local positioning error and the position fusion error. In an attempt to improve the positioning result in both local positioning accuracy and global convergence probability, this paper proposes a novel MDS-MAP(LF) algorithm, which uses low frequency signal to measure the inter-sensor distance rather than shortest path algorithms. The proposed MDS-MAP(LF) algorithm leverages the propagation feature of low frequency signal to acquire a more precisely two-hop distance. The simulation and analysis results indicate that the accuracy of local positioning is improved by more than 3%. With the use of cluster expansion, MDS-MAP(LF) also shows a better convergence with comparison to the former classical distributed MDS-MAP algorithm.

A Distributed MDS-MAP Algorithm for Wireless Sensor Networks

Aiming at the deficiencies of positioning accuracy and matrix operating complexity in the classical MDS-MAP locating algorithm in WSN, a distributed multi-dimensional scaling-MAP algorithm (MDS-MAP (D)) was proposed in this paper. In this algorithm, the distance of the nodes was quantified to obtain the close-neighbor vector (CNV), the distance matrix D and the initial value of nodes location was calculated. Theoretical analysis and simulation experimental results show that, the advance algorithm can realize the positioning of sensor nodes with good accuracy.

Distributed sensor network localization from local connectivity

This paper addresses the problem of determining the node locations in ad-hoc sensor networks when only connectivity information is available. In previous work, we showed that the localization algorithm MDS-MAP proposed by Y. Shang et al. is able to localize sensors up to a bounded error decreasing at a rate inversely proportional to the radio range r. The main limitation of MDS-MAP is the assumption that the available connectivity information is processed in a centralized way. In this work we investigate a practically important question whether similar performance guarantees can be obtained in a distributed setting. In particular, we analyze the performance of the HOP-TERRAIN algorithm proposed by C. Savarese et al. This algorithm can be seen as a distributed version of the MDS-MAP algorithm. More precisely, assume that the radio range r=o(1) and that the network consists of n sensors positioned randomly on a d-dimensional unit cube and d+1 anchors in general positions. We show that when only connectivity information is available, for every unknown node i, the Euclidean distance between the estimate x i and the correct position x i is bounded by ||x i -x i || < r 0 /r + o(1), where r 0 =C d (log n/ n) (1/d) for some constant C d which only depends on d. Furthermore, we illustrate that a similar bound holds for the range-based model, when the approximate measurement for the distances is provided.

Positioning using local maps

It is often useful to know the positions of nodes in a network. However, in a large network it is impractical to build a single global map. In this paper, we present a new approach for distributed localization called Positioning using Local Maps (PLM). Given a path between a starting node and a remote node we wish to localize, the nodes along the path each compute a map of their local neighborhood. Adjacent nodes then align their maps, and the relative position of the remote node can then be determined in the coordinate system of the starting node. Nodes with known positions can easily be incorporated to determine absolute coordinates. We instantiate the PLM framework using the previously proposed MDS-MAP(P) algorithm to generate the local maps. Through simulation experiments, we compare the resulting algorithm, which we call MDS-MAP(D), with existing distributed methods and show improved performance on both uniform and irregular topologies.