Efficient Multisensor Localization for the Internet of Things: Exploring a New Class of Scalable Localization Algorithms

Win, Moe Z.; Meyer, Florian; Liu, Zhenyu; Dai, Wenhan; Bartoletti, Stefania; Conti, Andrea

doi:10.1109/msp.2018.2845907

Cited by 128 publications

(43 citation statements)

References 30 publications

(78 reference statements)

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“…5G networks will allow connecting large number of stationary and mobile devices, sensors, machines, and supporting Internet of Things (IoT) [115,116].…”

Section: Cooperative Localization In 5g Networkmentioning

confidence: 99%

A survey on 5G massive MIMO localization

Wen

Wymeersch

Peng

et al. 2019

Digital Signal Processing

110

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“…5G networks will allow connecting large number of stationary and mobile devices, sensors, machines, and supporting Internet of Things (IoT) [115,116].…”

Section: Cooperative Localization In 5g Networkmentioning

confidence: 99%

A survey on 5G massive MIMO localization

Wen

Wymeersch

Peng

et al. 2019

Digital Signal Processing

110

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show abstract

“…However, the new characteristics of IoT networks, such as the long-range, limited number of messages, and the inexpensive IoT nodes, bring extra localization challenges. Therefore, the localization problem in IoT networks has recently attracted considerable research focus [4,17]. Among all localization techniques, RSSI-based methods have been proven as one of the effective solutions [18]- [24].…”

Section: A Related Workmentioning

confidence: 99%

Localization in Ultra Narrow Band IoT Networks: Design Guidelines and Tradeoffs

Sallouha

Chiumento

Rajendran

et al. 2019

IEEE Internet Things J.

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Localization in long-range Internet of Things networks is a challenging task, mainly due to the long distances and low bandwidth used. Moreover, the cost, power, and size limitations restrict the integration of a GPS receiver in each device. In this work, we introduce a novel received signal strength indicator (RSSI) based localization solution for ultra narrow band (UNB) long-range IoT networks such as Sigfox. The essence of our approach is to leverage the existence of a few GPSenabled sensors (GSNs) in the network to split the wide coverage into classes, enabling RSSI based fingerprinting of other sensors (SNs). By using machine learning algorithms at the network backed-end, the proposed approach does not impose extra power, payload, or hardware requirements. To comprehensively validate the performance of the proposed method, a measurement-based dataset that has been collected in the city of Antwerp is used. We show that a location classification accuracy of 80% is achieved by virtually splitting a city with a radius of 2.5 km into seven classes. Moreover, separating classes, by increasing the spacing between them, brings the classification accuracy up-to 92% based on our measurements. Furthermore, when the density of GSN nodes is high enough to enable device-to-device communication, using multilateration, we improve the probability of localizing SNs with an error lower than 20 m by 40% in our measurement scenario.

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“…Improved communication strategies can reduce the number of message exchanges. Multi-agent localization technology is required in many scenarios [24][25][26], and it is also necessary to improve the efficiency of multi-agent localization [27,28]. TDOA-based localization methods can reduce the number of message exchanges between nodes while completing ranging and localization [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems

2019

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Ultra-wideband technology has the merits of high temporal resolution and stability, and it has been widely used for high-accuracy localization and tracking. However, most ultra-wideband localization systems need multiple anchors for trilateration, which results in high system cost, large messages overhead, and insufficient extraction of information. In this paper, we propose a single-anchor localization (SAL) mehtod that achieves high-accuracy multi-agent localization with high efficiency. In the proposed method, the anchor broadcasts the first two messages and then each agent responds one message to the anchor (quasi-)simultaneously. Based on the received message with superpositioned agent responses, the time-of-flight and angle-of-arrival information from all agents to the anchor can be extracted altogether. We implement the localization system in two indoor environments, and show that the proposed method can achieve decimeter-level accuracy for multiple agents using three messages. Our method provides design guidelines for high-accuracy and high-efficiency multi-agent localization systems.

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Efficient Multisensor Localization for the Internet of Things: Exploring a New Class of Scalable Localization Algorithms

Cited by 128 publications

References 30 publications

A survey on 5G massive MIMO localization

A survey on 5G massive MIMO localization

Localization in Ultra Narrow Band IoT Networks: Design Guidelines and Tradeoffs

An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems

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