ER-CRLB: An Extended Recursive Cramér–Rao Lower Bound Fundamental Analysis Method for Indoor Localization Systems

Zhao, Y. B.; Fan, Xiaopeng; Xu, Cheng‐Zhong; Li, Xiaofan

doi:10.1109/tvt.2016.2553682

Cited by 28 publications

(15 citation statements)

References 27 publications

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“…Generally, localization techniques are split into two categories, geometrical and non-geometrical techniques. In geometric techniques, the position of the MN is estimated by compiling one or more channel characteristics, such as angle of arrival (AoA) [10], time difference of arrival (TDoA) [11], time of arrival (ToA) [12], or received signal strength indicator (RSSI) [13][14][15] into a geometric output. Equations relating the unknown position of the MN with the known positions of the ANs are derived and solved to estimate the MN position.…”

Section: Measurement Techniquesmentioning

confidence: 99%

An Evidential Framework for Localization of Sensors in Indoor Environments

Alshamaa

Mourad-Chehade

Honeiné

et al. 2020

Sensors

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Indoor localization has several applications ranging from people tracking and indoor navigation, to autonomous robot navigation and asset tracking. We tackle the problem as a zoning localization where the objective is to determine the zone where the mobile sensor resides at any instant. The decision-making process in localization systems relies on data coming from multiple sensors. The data retrieved from these sensors require robust fusion approaches to be processed. One of these approaches is the belief functions theory (BFT), also called the Dempster–Shafer theory. This theory deals with uncertainty and imprecision with a theoretically attractive evidential reasoning framework. This paper investigates the usage of the BFT to define an evidence framework for estimating the most probable sensor’s zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods.

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Section: Measurement Techniquesmentioning

confidence: 99%

An Evidential Framework for Localization of Sensors in Indoor Environments

Alshamaa

Mourad-Chehade

Honeiné

et al. 2020

Sensors

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“…For example, when all the mobile sensors are in random movement in a fully mobile sensor network, packet routing and information dissemination will be too complicated to be practical [ 3 ]. Effective coverage is one of the key problems in sensor networks, and can be applied to various applications like localization problems, where sensors are deployed to achieve minimum estimation error [ 4 ]. Moving mobile sensors to meet network coverage requirement in hybrid WSNs has received a lot attention recently.…”

Section: Related Workmentioning

confidence: 99%

A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks

Zhang

Fok

2017

Sensors

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Providing field coverage is a key task in many sensor network applications. In certain scenarios, the sensor field may have coverage holes due to random initial deployment of sensors; thus, the desired level of coverage cannot be achieved. A hybrid wireless sensor network is a cost-effective solution to this problem, which is achieved by repositioning a portion of the mobile sensors in the network to meet the network coverage requirement. This paper investigates how to redeploy mobile sensor nodes to improve network coverage in hybrid wireless sensor networks. We propose a two-phase coverage-enhancing algorithm for hybrid wireless sensor networks. In phase one, we use a differential evolution algorithm to compute the candidate’s target positions in the mobile sensor nodes that could potentially improve coverage. In the second phase, we use an optimization scheme on the candidate’s target positions calculated from phase one to reduce the accumulated potential moving distance of mobile sensors, such that the exact mobile sensor nodes that need to be moved as well as their final target positions can be determined. Experimental results show that the proposed algorithm provided significant improvement in terms of area coverage rate, average moving distance, area coverage–distance rate and the number of moved mobile sensors, when compare with other approaches.

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“…For example, timely communication of the sensed data allows the users to make real-time decisions preventing potential financial or human loss. Due to these reasons, IOUT is being employed in many applications such as precision agriculture , border monitoring [38,39], landslide and pipeline monitoring [5,40,41], indoor localization system [42], and power-efficient wireless positioning systems [43].…”

Section: Introductionmentioning

confidence: 99%

Wireless Underground Communications in Sewer and Stormwater Overflow Monitoring: Radio Waves through Soil and Asphalt Medium

Raza

Salam

2020

Information

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Storm drains and sanitary sewers are prone to backups and overflows due to extra amount wastewater entering the pipes. To prevent that, it is imperative to efficiently monitor the urban underground infrastructure. The combination of sensors system and wireless underground communication system can be used to realize urban underground IoT applications, e.g., storm water and wastewater overflow monitoring systems. The aim of this article is to establish a feasibility of the use of wireless underground communications techniques, and wave propagation through the subsurface soil and asphalt layers, in an underground pavement system for storm water and sewer overflow monitoring application. In this paper, the path loss analysis of wireless underground communications in urban underground IoT for wastewater monitoring has been presented. The dielectric properties of asphalt, sub-grade aggregates, and soil are considered in the path loss analysis for the path loss prediction in an underground sewer overflow and wastewater monitoring system design. It has been shown that underground transmitter was able to communicate through thick asphalt (10 cm) and soil layers (20 cm) for a long range of up to 4 km.Information 2020, 11, 98 2 of 11 towards building a sustainable urban underground infrastructure. To that end, the works in [45,46] list physical characteristics that need to be considered while planning urban underground infrastructure, the authors of [47] list changes required to improve current urban underground infrastructure, and the authors of [48] use ground-penetrating radar (GPR) technology for detecting damage in underground pipes. Finally, the authors of [49] provide a detailed survey that studies various urban indicator (systems) and argues the inclusion of urban underground space into the list of indicators.Wastewater facilities make up an important part of an urban infrastructure. City governments establish facilities for collection and treatment of the wastewater. These facilities process millions of gallons of wastewater on a daily basis. The main purpose of these facilities is to look after the quality and quantity of wastewater being collected at the collection system and reaching recovering facilities. It is important because, if not monitored properly, thousands of gallons of water flowing underground can result in overflow of sanitary sewers. Therefore, a smart solution is required for timely detection and prevention of any disaster occurring due to inefficient monitoring of wastewater. However, city governments do not have affordable underground communication and sensor technologies. Moreover, there exist limited cost-efficient underground IoT solutions because of connectivity challenges in UG environment. Another issue is the extensive cabling required to connect with existing over-the-air communication solutions [50,51]. To that end, the contribution of this paper is to improve the connectivity and communication in an underground environment, which is achieved by performing the path loss analysis for ur...

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ER-CRLB: An Extended Recursive Cramér–Rao Lower Bound Fundamental Analysis Method for Indoor Localization Systems

Cited by 28 publications

References 27 publications

An Evidential Framework for Localization of Sensors in Indoor Environments

An Evidential Framework for Localization of Sensors in Indoor Environments

A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks

Wireless Underground Communications in Sewer and Stormwater Overflow Monitoring: Radio Waves through Soil and Asphalt Medium

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