![]() One challenge is that, in the SPAWN algorithm, the positions of agents are estimated by iterative belief exchange. In this paper, we propose to modify a conventional cooperative localization approach named the sum–product algorithm over a wireless network (SPAWN) for an improved localization performance for IoT WSNs, which aims to address two main challenges. Therefore, cooperative localization is a better choice, especially when the number of anchors is limited by some practical considerations. In most localization scenarios for IoT WSNs, the number of agents is generally much more than that of anchors. 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 . 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 . 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 . In the location-aware WSN, the network is composed of anchors with known positions and agents with unknown positions. An attractive choice is to use the location-aware WSN . Hence, alternative localization methods for IoT WSNs need to be studied. However, in some harsh environments such as indoors or urban canyons, GPS signals may suffer obstacle shadowing, resulting in an inefficient target localization . Nowadays, the most popular technology to provide location-awareness is the global positioning system (GPS). Such applications in WSNs for IoT are mostly based on the sensor’s location . ![]() 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 , as shown in Fig. 1. Furthermore, the localization accuracy is improved with the proposed NLOS mitigation algorithm, especially in more severe NLOS environments.Īs a revolutionary paradigm of communication, Internet-of-Things (IoT) has attracted intensive interest for many promising applications . Numerical results show that by virtue of the proposed node selection algorithm, the energy consumption of the network is significantly reduced. 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. 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. We propose a node selection algorithm and an NLOS mitigation algorithm to modify the conventional cooperative localization algorithm for an improved performance. 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. 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.
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