Greedy Successive Anchorization for Localizing Machine Type Communication Devices

Abstract

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.

Mobile Location in Wireless Sensor Networks Based on Multi Spot Measurements Model

“…Although many CRLBs for various location techniques considering one-spot measurements have been presented in the literature [ 16 , 17 , 18 , 23 , 24 , 25 ], to the best of our knowledge, performance analysis for localization has not been carried out on a multi-spot measurement model in WSNs. This section provides performance analysis for localization using multi-spot measurements in terms of CRLB.…”

“…Many localization methods are presented in the literature [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. There are two ways to locate a sensor in WSNs: (1) range-based and (2) range-free methods.…”

“…Various iterative algorithms, including Newton–Raphson and convex optimization methods [ 11 , 12 , 13 , 14 ], have been developed to obtain optimal performance. Besides LOS location methods, performance analysis in terms of CRLBs is also provided in the literature [ 16 , 17 , 18 ]. Non-line-of-sight (NLOS) presents a major problem in determining accurate location in WSNs, prompting the development of many positioning methods [ 19 , 20 , 21 , 22 ] and CRLBs [ 23 , 24 , 25 ] for NLOS propagation in the literature.…”

“…Although many studies are reported for both range-based and range-free location methods [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ], they are all based on a one-spot measurement model, as shown in Figure 1 . Since the existing location methods have been widely studied, it is difficult to improve their positioning accuracy further.…”