Elsevier

Ad Hoc Networks

Volume 118, 1 July 2021, 102504
Ad Hoc Networks

On improving the cooperative localization performance for IoT WSNs

https://doi.org/10.1016/j.adhoc.2021.102504Get rights and content

Abstract

The emergence of IoT technology makes the applications of IoT wireless sensor networks (WSNs) attract more attentions, and most of the applications are based on the sensor’s location. This paper addresses two main challenges encountered in the localization issue in IoT WSNs, i.e., limited energy in battery-powered sensors, and non-line-of-sight (NLOS) induced errors in harsh environments. We propose a node selection algorithm and an NLOS mitigation algorithm to modify the conventional cooperative localization algorithm for an improved performance. In the node selection algorithm, a node selection criterion is designed to select the most informative reference nodes for each agent, avoiding unnecessary energy consumption. An N-probabilistic hard weight (N-PHW) strategy is developed in the NLOS mitigation algorithm, in which a link condition indicator is set to quantify the quality of each link and NLOS errors are penalized based on this indicator. Numerical results show that by virtue of the proposed node selection algorithm, the energy consumption of the network is significantly reduced. Furthermore, the localization accuracy is improved with the proposed NLOS mitigation algorithm, especially in more severe NLOS environments.

Introduction

As a revolutionary paradigm of communication, Internet-of-Things (IoT) has attracted intensive interest for many promising applications [1], [2], [3]. With the collected data around physical environment, the ‘things’ in IoT can form a wireless sensor network (WSN) to provide services for various applications, such as smart city, environmental monitoring, e-Health and asset tracking [4], as shown in Fig. 1. Such applications in WSNs for IoT are mostly based on the sensor’s location [5]. Nowadays, the most popular technology to provide location-awareness is the global positioning system (GPS). However, in some harsh environments such as indoors or urban canyons, GPS signals may suffer obstacle shadowing, resulting in an inefficient target localization [6]. Hence, alternative localization methods for IoT WSNs need to be studied. An attractive choice is to use the location-aware WSN [7].

In the location-aware WSN, the network is composed of anchors with known positions and agents with unknown positions. The anchors provide reference information for localizing the agents, and the corresponding localization algorithms can be typically divided into two major groups: non-cooperative and cooperative methods [8], [9]. In the non-cooperative localization, only the distance measurements between the agents and the anchors are utilized for localization; while in the cooperative localization, the distance measurements between the agents are also included in the positioning process [10]. It has been proved that cooperative localization algorithms can commonly achieve a better performance than their non-cooperative counterparts by virtue of sharing the positional information among agents [11]. In most localization scenarios for IoT WSNs, the number of agents is generally much more than that of anchors. Therefore, cooperative localization is a better choice, especially when the number of anchors is limited by some practical considerations. In this paper, we propose to modify a conventional cooperative localization approach named the sum–product algorithm over a wireless network (SPAWN) [12] for an improved localization performance for IoT WSNs, which aims to address two main challenges.

One challenge is that, in the SPAWN algorithm, the positions of agents are estimated by iterative belief exchange. Hence, the energy consumption may be increased drastically as more agents engage in the positioning. In fact, in the classical cooperative localization algorithms, all the agents within the communication range broadcast their location information to others at each iteration, resulting in increased energy consumption [13]. If the agents exchange information too often, it will incur heavy network traffic. For a network with a large node density, the increased participated nodes may also cause a huge computational complexity, which is disadvantageous for some resource-constrained networks [14]. Therefore, appropriately selecting nodes for localization is beneficial not only to the energy conservation, but also the complexity reduction in cooperative algorithms [15]. In [16], a distributed scheduling algorithm based on information evolution is proposed, which only selects the most valuable links rather than the whole set of links, in order to reduce the communication overhead. In [17], the node selection problem is formulated into a network formation game among the agents, where the most informative links are selected by maximizing the utility of the localized node. On the basis of the existing research, this paper extracts several of the most important influencing factors to construct the selection criteria and selects the most favorable beacons for each agent in the SPAWN algorithm, so as to reduce the energy consumption.

The other challenge is that, in the raw SPAWN algorithm, the NLOS problem has not been considered. However, in the popular application scenarios of IoT WSNs such as indoors, there is a high occurrence of NLOS situation. Due to the presence of walls or other obstacles, the estimated distance of path in NLOS situation is commonly larger than the actual one. Such positively biased distance estimation may seriously degrade the positioning accuracy [18]. Identifying the NLOS propagations and then discarding these measurements directly is a simple solution to deal with NLOS problems [19]. However, such identify and discard (IAD) approaches may cause a waste of some valuable NLOS measurements. Actually, when prior NLOS statistics are available, a higher positioning accuracy can be achieved by appropriately using the NLOS measurements [20]. Hence, the NLOS mitigation methods are proposed to mitigate the negative impact of NLOS biases and take advantage of all the range estimations. In [21], by assuming NLOS errors as the exponentially distributed parameters, an approximate maximum likelihood (AML) estimation problem is formulated and a computationally efficient semi-definite programming (SDP) approach is proposed to solve it. The authors in [22] propose to jointly estimate the source position and the NLOS error in the reference path, and efficiently solve the optimization problem via two new robust least squares (RLS) methods. The above work provides powerful solutions for dealing with NLOS situations in the non-cooperative localization. In this paper, we will focus on the NLOS problems in the cooperative localization, and modify the SPAWN algorithm into the one with the ability of NLOS mitigation by weighting the usage of NLOS measurements.

The main contributions of this paper can be summarized as follows.

  • An improved cooperative localization algorithm is proposed for IoT WSNs, which modifies the conventional SPAWN algorithm into the one with a reduced energy consumption and also with the ability of NLOS mitigation.

  • A node selection algorithm is proposed to find the most informative beacon set for each localized sensor. The selection criteria are set and a selection mechanism is designed to find the localization node set with the largest utility according to the proposed merge and split principles.

  • To alleviate the NLOS-induced biases and make full use of all the range measurements, an NLOS mitigation algorithm is proposed to improve the localization performance in terms of positioning accuracy. An N-probabilistic hard weight (N-PHW) strategy is designed whereby each link is weighted according to its degree of NLOS deterioration.

  • Simulation results show that the proposed algorithm can reduce the energy consumption of the network and achieve a good compromise between convergence speed and positioning accuracy.

The remainder of this paper is organized as follows. Section 2 elaborates the system model. In Section 3, the node selection criteria as well as the selection mechanism are proposed. Section 4 provides the NLOS mitigation strategy for the modified SPAWN algorithm. Finally, simulation results are presented in Section 5, and conclusions are drawn in Section 6.

Notations

The bold characters a and A denote a vector and a matrix, respectively. R denotes the set of real numbers. tr{A} denotes the trace of a square matrix A. AB means that matrix AB is positive semidefinite.

Section snippets

System model

Consider a IoT WSN in a mixed LOS and NLOS scenario with M sensor nodes,1 in which there are Ma agents and MMa anchors. For anchors, their positions are exactly known while the agents’ positions need to be estimated through cooperative localization. The position of node i is indicated by xi=(xi,yi)TR2 in the 2-D node localization system. The set of nodes with which agent i can

Node selection algorithm for cooperative localization

This section gives the criteria for node selection and proposes the node selection algorithm based on the criteria via the merge and split principles.

Modified SPAWN algorithm with NLOS mitigation

This section proposes to modify the conventional cooperative localization algorithm named SPAWN. An N-probabilistic hard weight (N-PHW) strategy is designed to mitigate the NLOS-induced biases of the distance estimation.

Simulation results and analysis

In this section, numerical simulations are conducted and corresponding analyses are provided to evaluate the performance of the proposed node selection algorithm, the NLOS mitigation algorithm and the modified SPAWN algorithm.

Conclusion

In this paper, a modified cooperative localization algorithm for IoT WSNs has been proposed by incorporating node selection and NLOS mitigation strategies into the raw SPAWN algorithm. In node selection, a selection criterion considering both the localization-accuracy contribution and the maintenance cost has been set, which can select the beacon set with the highest utility for localizing the agent. In NLOS mitigation, an N-PHW strategy has been developed to mitigate the NLOS propagations. In

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the Shanghai Sailing Program, China (No. 20YF1452700), and in part by the National Natural Science Foundation of China (No. 61936014), and was also supported by the National Key Research and Development Program of China (No. 2019YFB2102300, No. 2019YFB2102301).

Yaping Zhu received the B.S. degree from the Southeast University, Nanjing, China, in 2012, and the Ph.D. degree in Information and Communications Engineering from the National Mobile Communications Research Laboratory, Southeast University in 2019. She was a visiting scholar to Georgia Institute of Technology from 2017 to 2018. She is currently the Assistant Professor with the college of Software Engineering, Tongji University, Shanghai, China. Her research interests include wireless

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    Yaping Zhu received the B.S. degree from the Southeast University, Nanjing, China, in 2012, and the Ph.D. degree in Information and Communications Engineering from the National Mobile Communications Research Laboratory, Southeast University in 2019. She was a visiting scholar to Georgia Institute of Technology from 2017 to 2018. She is currently the Assistant Professor with the college of Software Engineering, Tongji University, Shanghai, China. Her research interests include wireless localization, software-defined networking, heterogeneous networks, and so on.

    Feng Yan received the B.S. degree from the Huazhong University of Science and Technology, Wuhan, China, in 2005, the M.S. degree from Southeast University, Nanjing, China, in 2008, and the Ph.D. degree from Telecom ParisTech, Paris, France, in 2013, all in electrical engineering. From 2013 to 2015, he was a Postdoctoral Researcher with Telecom Bretagne, Rennes, France. He is currently an Associate Professor with the National Mobile Communications Research Laboratory, Southeast University. His current research interests include wireless communications and wireless networks, with an emphasis on applications of homology theory and stochastic geometry in wireless networks.

    Shengjie Zhao (Senior Member, IEEE) received the B.S. degree in electrical engineering from the University of Science and Technology of China, Hefei, China, in 1988, the M.S. degree in electrical and computer engineering from the China Aerospace Institute, Beijing, China, in 1991, and the Ph.D. degree in electrical and computer engineering from Texas A&M University, College Station, TX, USA, in 2004. He is currently the Dean of the College of Software Engineering, a Professor with the College of Software Engineering and the College of Electronics and Information Engineering, Tongji University, Shanghai, China. In previous postings, he has conducted research with Lucent Technologies, Whippany, NJ, USA, and the China Aerospace Science and Industry Corporation, Beijing. His research interests include artificial intelligence, big data, wireless communications, image processing, and signal processing. He is a Fellow of the Thousand Talents Program of China.

    Song Xing received the B.S. and M.S. degrees from Southeast University, China, and the Ph.D. degree from George Mason University, Virginia, USA, in 2003, all in electrical & computer engineering. He is currently the Professor in the Information Systems Department, California State University, Los Angeles, USA. His research interests include the Internet statistical measurement, wireless and mobile communications, and speech & image processing.

    Lianfeng Shen (Senior Member, IEEE) received the B.S. degree in radio technology and the M.S. degree in wireless communications from Southeast University, Nanjing, China, in 1978 and 1982, respectively, where he joined the Department of Radio Engineering, in 1982. From 1991 to 1993, he was a Visiting Scholar and a Consultant with the Hong Kong Productivity Council working on wireless communications. In 1998, he was a Senior Consultant with the Telecom Technology Centre of Hong Kong. Since 1997, he has been a Professor with the National Mobile Communications Research Laboratory, Southeast University. His research interests include broadband mobile communications, including broadband wireless access systems, vehicular ad hoc networks, communications protocols, and so on. He serves as the Chair of the IEEE Communication Society Nanjing Chapter.

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