Journal of Sensors

Volume 2018, Article ID 5190543, 16 pages

https://doi.org/10.1155/2018/5190543

## Heuristic Localization Algorithm with a Novel Error Control Mechanism for Wireless Sensor Networks with Few Anchor Nodes

Ministerial Key Laboratory of ZNDY, Nanjing University of Science and Technology, Nanjing 210094, China

Correspondence should be addressed to Xiaoming Wang; moc.361@mx202

Received 11 November 2017; Revised 23 May 2018; Accepted 7 June 2018; Published 11 July 2018

Academic Editor: Bruno Andò

Copyright © 2018 Yujia Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

A novel iterative localization algorithm with high accuracy and low anchor node dependency for large-scale wireless sensor networks is proposed in this paper. At each iteration, blind nodes are located using a weighted linear least squares-based algorithm. To prevent errors in the blind nodes from propagating and accumulating throughout the network, an anchor geometric feature-based error control mechanism is used to select the nodes that participate in the localization and to estimate the localization confidence. The simulation results show that the algorithm can be used when only a few anchor nodes are involved. This algorithm is more advanced than traditional methods, which often require a large number of well-placed anchor nodes to operate appropriately. By optimizing the decision parameter of the algorithm, the average localization error of the algorithm is approximately 0.43 meters. When the ratio of anchor nodes (the ratio of the number of anchor nodes to the number of sensor nodes in the network) is 1.25% (i.e., 5 anchor nodes for 400 sensor nodes), the received signal strength indicator (RSSI) variance is 8 dBm, and the radio range is 50 meters. A comparison of the proposed algorithm with global localization methods, including multidimensional scaling (MDS), semidefinite programming (SDP), and shortest-path access (SPA), shows that the proposed algorithm achieves higher location accuracy and stability when the number of anchor nodes is varied. The efficiency of the proposed localization algorithm is evaluated in a real sensor network, and the accuracy is high and robust to radio channel variance.

#### 1. Introduction

Wireless sensor networks (WSNs) are a basic component of the Internet of Things. Based on the development of mobile computing and embedded technologies, WSNs have been widely implemented in daily applications, such as health care monitoring, natural disaster prevention, and surveillance [1–3]. Sensor network services, such as mobile sinks, geographic routing, and location-based multicasting, rely on the physical location information of sensor nodes or the phenomena of interest. One method of accessing the locations of nodes is to use the Global Positioning System (GPS). However, installing a GPS chip on every sensor node is expensive. Moreover, the GPS is not always available because of environmental or hardware limitations, such as shadowing, energy, volume, or cost issues. Therefore, efficient and inexpensive localization technologies must be developed [4, 5].

Various WSN localization approaches have been presented in the literature. Depending on the usage of the range information between nodes, node localization approaches can be classified into range-based localization and range-free localization. In a range-based localization method, the blind nodes estimate the distance to each anchor node and then estimate the location based on the distance to and position of the anchor nodes. Anchor nodes are a special type of sensor node with known locations, with the help of GPS, or are predeployed. Blind nodes are sensor nodes with unknown positions. To estimate distance, blind nodes use radio channel information, such as the received signal strength indicator (RSSI) [6, 7], time of arrival (ToA) [8, 9], time difference of arrival (TDoA) [10, 11], angle of arrival (AoA) [12], or some combination of these methods. The RSSI-based distance estimation is attractive because of its low cost, long range, and simplicity. After determining the distance between blind nodes and anchor nodes, multilateration technology is used for position estimation [13, 14]. Trilateration is a particular form of multilateration that utilizes three anchor nodes to calculate the location of a blind node in two dimensions. For a given WSN, to estimate the position of all blind nodes, at least three anchor nodes are required in the communication range of the blind node. For a large-scale outdoor randomly deployed WSN, a large number of anchor nodes should be deployed to guarantee that every blind node is located. However, additional anchor nodes correspond to additional costs associated with hardware, deployment, maintenance, and so on.

Iterative node localization is a novel localization method that can decrease the dependence on anchor nodes by importing a blind node leveling-up scheme [15, 16]. For iterative node localization, the located blind node is upgraded to new anchor nodes (pseudoanchor) in which the location information of the initial anchor nodes is progressively propagated to other nodes. Theoretically, all blind nodes in a network can be located with three anchor nodes under an iterative localization method. In practical localization systems, measurement noise during node ranging is inevitable, and the location of a pseudoanchor is uncertain. As other blind nodes locate themselves and refer to the pseudoanchors with uncertain locations, the localization error increases as more pseudoanchors are introduced into the localization. This process is called the error propagation problem in iterative node localization [17]. Various methods have been proposed to mitigate the propagation error. Liu et al. [18] proposed an error control mechanism that tracks the estimation error and quantifies each location estimation with a given level of uncertainty. Based on the location certainty information, the algorithm introduces a neighbor selection procedure and an update criterion. The simulation results show that the localization accuracy and robustness are significantly improved with this approach. Yang and Liu [19] proposed the concept of the quality of trilateration (QoT), which considers both the geometric relationship and ranging errors. Based on the QoT, a confidence-based iterative localization (CIL) scheme can be designed. At each stage, the CIL selectively utilizes trilaterations and reduces the likelihood of using low-confidence references, which effectively halts error propagation. Wu et al. [20] considered the error control problem for non-Gaussian noise measurements. The proposed error control algorithm estimates the location error based on nonlinear least squares residuals. Additionally, a robust formulation of error control is proposed that can reduce the accumulated error. The simulations show that more anchors, less noise, and fewer iterations reduce the localization error. Hu et al. [21] derived an upper bound of error propagation for iterative localization that works well when the precise environmental noise distribution is unavailable. The minimum upper bound is adopted to evaluate the localization result. With this method, the algorithm constructs the proper linear least squares localization with high probability. The main concept underlying these methods is the estimation of the ranging error. The algorithms exhibit good localization accuracy when the noise is precisely estimated.

In this paper, we analyze the localization problem to reduce anchor node dependency in a large-scale WSN. A novel iterative localization algorithm with an anchor geometric feature-based error control mechanism is proposed. Compared with previous research that precisely modeled the localization error during each iteration, we formulate the error control problem as a classification problem. Based on node localization data under varying ratios of anchor nodes, RSSI variances, and radio communication ranges in a randomly deployed node environment, we characterize the localization condition by considering the number of anchor nodes, the anchor positions, the distance between the anchors and blind nodes, and the spatial distribution of anchors. Additionally, a one-class support vector decision-maker is trained based on the selected features. The simulation results show that the algorithm can be used when only a few anchor nodes are involved, which is more advanced than traditional methods that often require a large number of well-placed anchor nodes to work well. We evaluate the performance of our algorithm with different numbers of anchor nodes and RSSI variances. The results show that the algorithm efficiently solves the error propagation problem. The average localization error of the algorithm is approximately 0.43 meters when the percentage of anchor nodes is 1.25%, the RSSI variance is 8 dBm, and the radio range is 50 meters. The location accuracy is better than those of certain global localization methods, including MDS, SDP, and SPA. The efficiency of the proposed localization algorithm is evaluated in a real sensor network, and the accuracy is high and robust to radio channel variance.

The remainder of this paper is organized as follows. In Section 2, we present the RSSI channel model and the weighted linear least squares location estimator. In Section 3, we present the anchor geometric features and the proposed error control mechanism. In Section 4, we evaluate the proposed localization algorithm with simulation experiments. In Section 5, we conclude the paper.

#### 2. Localization Method

In this paper, we classify wireless sensor nodes as blind nodes, anchor nodes, pseudoanchor nodes, and reference nodes based on their localization duty. These names will be discussed in the following sections, and their respective duties are explained at the beginning of the paper. Anchor nodes are nodes that can access their absolute location at the beginning of the deployment. Blind nodes are nodes that do not know their location and attempt to estimate it based on the locations of the anchor nodes. A pseudoanchor node is a blind node that has an estimated location and is upgraded to an anchor node. The location of the pseudoanchor node always has an error, and the algorithm should be designed to stop the error from propagating throughout the network. The reference node is a node used by the blind node to locate itself. Both the anchor nodes and the pseudoanchor nodes can be used as reference nodes.

##### 2.1. RSSI Signal Model

Many channel models have been proposed in the literature for indoor or outdoor localization applications [22]. In this paper, the log-normal shadowing model is used for RSSI-based localization because of its simplicity. Let denote the received power and denote the distance between the transmitter and the receiver. Then, the log-normal shadowing model is expressed as follows: where is the path loss at distance , is the path loss exponent, and is a zero mean random variable with variance . In general, is equal to 1 meter, and is in the range of 2 to 4. The parameters and should be measured through real experiments. In this paper, we conduct the experiment based on the TI CC2650 sensor node in an outdoor environment. The results are shown in Figure 1. This channel model is used in the following section.