Abstract
In the era of the Internet of Things (IoT), efficient localization is essential for emerging mass-market services and applications. IoT devices are heterogeneous in signaling, sensing, and mobility, and their resources for computation and communication are typically limited. Therefore, to enable location awareness in large-scale IoT networks, there is a need for efficient, scalable, and distributed multisensor fusion algorithms. This article presents a framework for designing network localization and navigation (NLN) for the IoT. Multisensor localization and operation algorithms developed within NLN can exploit spatiotemporal cooperation, are suitable for arbitrary, largenetwork sizes, and only rely on an information exchange among neighboring devices. The advantages of NLN are evaluated in a large-scale IoT network with 500 agents. In particular, because of multisensor fusion and cooperation, the presented network localization and operation algorithms can provide attractive localization performance and reduce communication overhead and energy consumption.
Highlights
L OCATION-AWARENESS [1]–[6] is a keystone of the Internet-of-Things (IoT) that fosters a wide range of emerging applications such as crowdsensing [7], big data analysis [8], environmental monitoring [9], and autonomous driving [10]
State-of-the-art multi-sensor fusion algorithms based on sequential Bayesian estimation (SBE) [11]–[13] are often impractical for IoT applications due to their decentralized network topology and the limited processing units of IoT devices
This paper provides an overview of the network localization and navigation (NLN) paradigm:
Summary
L OCATION-AWARENESS [1]–[6] is a keystone of the Internet-of-Things (IoT) that fosters a wide range of emerging applications such as crowdsensing [7], big data analysis [8], environmental monitoring [9], and autonomous driving [10]. The high number of devices necessitates network operation strategies to provide inter-device cooperation for an efficient use of the limited battery power and spectral resources For these reasons, the major difficulties for efficient multi-sensor localization and navigation in the IoT lie in fusing data and measurements collected from heterogeneous sensors with low computation and communication capabilities and in designing network operation strategies that can efficiently allocate resources in scenarios with insufficient infrastructure and limited battery power. The major difficulties for efficient multi-sensor localization and navigation in the IoT lie in fusing data and measurements collected from heterogeneous sensors with low computation and communication capabilities and in designing network operation strategies that can efficiently allocate resources in scenarios with insufficient infrastructure and limited battery power Addressing these difficulties can overcome the key issues in the current IoT networks, including the heterogeneity of sensing technologies and the limited capability of devices in terms of computation, communication, and battery energy.
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