Accuracy Enhancement of RSSI-based Distance Estimation by Applying Gaussian Filter

Lee, Jea Gu; Kim, Byung-Kwan; Jang, Sung Bong; Yeon, Seung Ho; Ko, Young Woong

doi:10.17485/ijst/2016/v9i20/94675

Cited by 14 publications

(10 citation statements)

References 10 publications

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“…While the spatial accuracy of GPS sensors are equivalent to those within dedicated GPS devices, in areas of poor GPS reception, smartphones can use Bluetooth and wireless signals to position themselves. The received signal strength indication (RSSI) methodology for wireless signals is accurate to within tens of meters, and matching wireless fingerprints of known locations further refines accuracy to within a few meters (Lawson 2012;Lee et al 2016;Swangmuang and Krishnamurthy 2008). Moreover, smartphones are capable of multiple readings per second.…”

Section: Technical Considerationsmentioning

confidence: 99%

An Overview of Technologies for Individual Trip History Collection: Mobility Decision Science Pillar SMART Mobility Consortium

Kwasnik

Carmichael

Isley

2019

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Section: Technical Considerationsmentioning

confidence: 99%

An Overview of Technologies for Individual Trip History Collection: Mobility Decision Science Pillar SMART Mobility Consortium

Kwasnik

Carmichael

Isley

2019

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“…Given at least three anchored devices, if distance to these anchored devices can be inferred from new RSS measurements, the location of the target can be easily calculated using simple geometry techniques such as hyperbolic and circular approaches. The hyperbolic approach converts the geometric problem in linear form and solves it through a closed form of the least square method [13,14]. On the other hand, the circular approach iteratively solves the optimization problem [15,16] through a gradient step.…”

Section: Related Workmentioning

confidence: 99%

Study of Activity Tracking through Bluetooth Low Energy-Based Network

Mohsin

Payandeh

Ho³

et al. 2019

Journal of Sensors

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This paper proposes a proof-of-concept, low-cost, and easily deployable Bluetooth low energy- (BLE-) based localization system which actively scans and localizes BLE beacons attached to mobile subjects in a room. Using the received signal strength (RSS) of a BLE signal and the uniqueness of BLE hardware addresses, mobile subjects can be identified and localized within the hospital room. The RSS measurement of the BLE signal from a wearable BLE beacon varies with distance to the wall-anchored BLE scanner. In order to understand and demonstrate the practicality of the relationship between RSS of a BLE beacon and the distance of a beacon from a scanner, the first part of the paper presents the analysis of the experiments conducted in a low-noise and nonreflective environment. Based on the analysis conducted in an ideal environment, the second half of the paper proposes a data-driven localization process for pinpointing the movements of the subject within the experimental room. In order to ensure higher accuracy like fingerprinting techniques and handle the increased number of BLE-anchored scanners like geometric techniques, the proposed algorithm was designed to combine the best aspects of these two techniques for better localization. The paper evaluates the effects of the number of BLE wall-mounted scanners and the number of packets on the performance of the proposed algorithm. The proposed algorithm locates the patient within the room with error less than 1.8 m. It also performs better than other classical approaches used in localization.

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“…Free-space path loss Fs Pl is defined as the reciprocal of the third factor present in (1); distance d is calculated as follows [36]:…”

Section: Cbc Algorithmmentioning

confidence: 99%

“…

λ = c / f

; c is the node velocity; and f is the frequency;

π = 3.14

; and d is the distance. From (1) RSSI (

R_{dB}

) is formulated as

R_{dB} = 10 \times \log \frac{P_{r}}{P_{t}}

Free‐space path loss

F s_{Pl}

is defined as the reciprocal of the third factor present in (1); distance d is calculated as follows [36]:

d = \frac{c}{4 π f} 10 \frac{F s_{Pl}}{20}

Aggregate the distance computed for randomly selected nodes, and then estimate centroid of each distance from that is determined from (3). The centroid is defined as the half of the distance values estimate, from this centroid optimal nodes are selected as the root node.…”

Section: Proposed Workmentioning

confidence: 99%

DDoS attack aware environment with secure clustering and routing based on RPL protocol operation

Alabsi

Anbar

Manickam

et al. 2019

IET Circuits, Devices & Systems

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Routeing protocol for low-power and lossy networks (RPL) are widely used in dynamic wireless sensor network. Several types of researches worked on RPL and designed hybrid energy-efficient cluster-parent-based RPL, queue utilisation (QU)-RPL, improved RPL, attack detection RPL etc. to support from vulnerable threats, provide efficient routeing and balance dynamic nodes in the network. Previous research findings dealt with certain limitations and problems. In this study, a new coordinative-balanced clustering algorithm is proposed which is enabled to balance destination-oriented directed acyclic graph (DODAG) that is formed in each cluster. Pruning of nodes from DODAG effectively maintains cluster with minimised complexity. This study also resolves the problem of two common security attacks such as distributed denial of service attack (DDoS) and version number attack. Then, enriched-ant colony optimisation is used for DDoS detection and a secure route is chosen to perform data transmission. At the instance of selecting best parent for forwarding data, the authors use residual energy and scoring factor. Scoring factor of each node denotes the legitimacy to be selected as the best parent. Finally, results obtained from Network Simulator 3 indicate better performances on the following metrics: packet delivery ratio, end-to-end delay, node death minimisation, packet loss rate and power consumption.

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Accuracy Enhancement of RSSI-based Distance Estimation by Applying Gaussian Filter

Cited by 14 publications

References 10 publications

An Overview of Technologies for Individual Trip History Collection: Mobility Decision Science Pillar SMART Mobility Consortium

An Overview of Technologies for Individual Trip History Collection: Mobility Decision Science Pillar SMART Mobility Consortium

Study of Activity Tracking through Bluetooth Low Energy-Based Network

DDoS attack aware environment with secure clustering and routing based on RPL protocol operation

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