An approach to infrastructure-independent person localization with an IEEE 802.15.4 WSN

Schmid, Johannes; Völker, Markus; Gädeke, Tobias; Weber, Pascal; Stork, Wilhelm; Müller-Glaser, Klaus D.

doi:10.1109/ipin.2010.5646831

Cited by 22 publications

(7 citation statements)

References 21 publications

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“…Strength Indicator (RSSI) methods for estimating the distance between two nodes (transmitter and receiver) assume a pathloss model [75], [76] between the transmitter and the receiver. The RSSI value of the received radio packet is used with the path-loss model to estimate the distance between sender and receiver as reported in [77].…”

Section: ) Rf Methods Distance Via Rssi: Received Signalmentioning

confidence: 99%

Review and Comparison of Spatial Localization Methods for Low-Power Wireless Sensor Networks

Iliev

Paprotny

2015

IEEE Sensors J.

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Spatial localization (co-location) of nodes in wireless sensor networks (WSNs) is an active area of research, with many applications in sensing from distributed systems such as micro-aerial vehicles, smart dust sensors, and mobile robotics. This paper provides a comprehensive review and comparison of recent implementations (commercial and academic) of physical measurement techniques used in sensor localization, and of the localization algorithms that apply these measurement techniques. Physical methods for measuring distances and angles between WSN nodes are reviewed, followed by a comprehensive comparison of localization accuracy, applicable ranges, node dimensions, and power consumption of the different implementations. A summary of advantages and disadvantages of each measurement technique is provided along with a comparison of co-localization methods in WSNs across multiple algorithms and distance ranges. A discussion of possible improvements to accuracy, range, and power consumption of selected self-localization methods is included in the concluding discussion. Although the preferred implementation depends on the application, required accuracy, and range, passive optical triangulation is reported as the most energy efficient localization method for low-cost/low-power miniature sensor nodes. It is capable of providing micron-level resolution, however the applicable range (inter-node distance) is limited to single centimeters.1530-437X (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2015.2450742, IEEE Sensors Journal SUBMITTED TO THE IEEE SENSORS JOURNAL 2 on 2.4 GHz radios are: Nordic Semiconductor nRF24L01 transceiver [16] with consumed power from 345 µW to 2.4 mW depending on the refresh rate of the location task, ZigBee Texas Instruments SoC CC2430 radio transceiver [12] with 10 mW to 40 mW consumption depending on implementation, RF Telosb transceivers [17] with consumption from 35 mW to 41 mW depending on the activity profile, and finally on custom radios for IEEE 802.15.4 build from digital ASIC standard cells technology [18] with power consumption of 15.6 mW at 0 dB output power. The communication system is used by a sensor node to perform its message-based self-localization task as well as for broadcasting the basic data collected by the sensor. Communication systems based on ultrasound waves have not been attempted due to the directional dependence of acoustic signals and their dependence on air temperature and humidity. Optical communication between sensors in WSN has been implemented recently using low-power laser diode emitters and detectors [19], modulating Corner-Cube Reflectors (CCR) [20], MEMS CCR arrays [21], MEMS steering mirors (gold coated, refl...

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Section: ) Rf Methods Distance Via Rssi: Received Signalmentioning

confidence: 99%

Review and Comparison of Spatial Localization Methods for Low-Power Wireless Sensor Networks

Iliev

Paprotny

2015

IEEE Sensors J.

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“…Schmid et al present a proof-of-concept of an ad hoc localization system for persons in a Wireless Sensor Network (WSN) [ 31 ]. Yamagushi et al present two approaches for collaborative indoor localization, which are the stop-and-go localization and the People-Centric Navigation (PCN) methods [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

Platform Architecture for Decentralized Positioning Systems

Kasmi

Norrdine

Blankenbach

2017

Sensors

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A platform architecture for positioning systems is essential for the realization of a flexible localization system, which interacts with other systems and supports various positioning technologies and algorithms. The decentralized processing of a position enables pushing the application-level knowledge into a mobile station and avoids the communication with a central unit such as a server or a base station. In addition, the calculation of the position on low-cost and resource-constrained devices presents a challenge due to the limited computing, storage capacity, as well as power supply. Therefore, we propose a platform architecture that enables the design of a system with the reusability of the components, extensibility (e.g., with other positioning technologies) and interoperability. Furthermore, the position is computed on a low-cost device such as a microcontroller, which simultaneously performs additional tasks such as data collecting or preprocessing based on an operating system. The platform architecture is designed, implemented and evaluated on the basis of two positioning systems: a field strength system and a time of arrival-based positioning system.

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“…This leads to poor localization accuracy due to additional signal attenuation resulting from transmission through walls and large obstacles [7]. These indoor localization techniques can be applied at different transmission technologies such as WiFi [15], Bluetooth Low Energy (BLE) [16], RFID [17], Ultra-wideband (UWB) [18], IEEE 802.15.4 [19], Infrared [20], Ultrasonic [21], and Zigbee [22]. In this paper, three types of transmission technologies are examined.…”

Section: Introductionmentioning

confidence: 99%

A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

2021

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Indoor localization has become one of the fundamental services that is required in a diverse set of applications these days, such as patient monitoring and smart parking. Highly accurate localization techniques impose high latency and high energy consumption on the underlying application system. Thus, for such indoor location-based application, offloading the computation of the localization process to a remote server with high resource capability has been recently introduced as an avenue to address such a challenge. In this paper, a computation offloading problem is formulated to find the optimal decision with regard to the operation of the localization process. This decision includes: a) Where to compute the localization task, either locally on the end device or on the edge server or on the cloud server, b) Which localization technique should be used, and finally, c) Which transmission technology is recommended to be chosen in combination with the localization technique. All these decisions are constrained by the device, and the servers resource capabilities load. They are also constrained by the fact that the localization algorithm has to satisfy a certain application QoS requirement. Within such context, three algorithms are proposed for task offload decision making. First, the Indoor Localization Latency Optimal Offloading algorithm, which finds the optimal offloading decision that minimizes the total latency of the system and is considered a benchmark for the other algorithms. Second, Indoor Localization Latency Centralized Offloading algorithm that finds a sub optimal solution with lower complexity. Third, Indoor Localization Latency Game-Theoretic Offloading decentralized algorithm that converges after finite improvement steps and achieves Nash equilibrium. Altogether, the paper finds the optimum localization strategy for all users with the minimum latency under mobile edge computing environment.

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An approach to infrastructure-independent person localization with an IEEE 802.15.4 WSN

Cited by 22 publications

References 21 publications

Review and Comparison of Spatial Localization Methods for Low-Power Wireless Sensor Networks

Review and Comparison of Spatial Localization Methods for Low-Power Wireless Sensor Networks

Platform Architecture for Decentralized Positioning Systems

A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

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