Empirical Characterization of Propagation Path Loss and Performance Evaluation for Co-Site Urban Environment

Okorogu, V N; U, Onyishi D.; C, Nwalozie G.; Utebor, N N

doi:10.5120/12001-7888

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…As determined in our experiments the value for A is different for the two scenarios (−87 for the first experiment and −67 for the second). As reported in existing literature [28,44], the value of n varies depending on room shapes and sizes, building materials, wall placement as well as furniture. This value was determined by a grid-search procedure, showing that the most suitable value for both scenarios is n = 2.5.…”

Section: Methodsmentioning

confidence: 88%

Indoor Localization Techniques Within a Home Monitoring Platform

Marin¹,

Bocicor²,

Molnar³

2020

Communications in Computer and Information Science

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This paper details a number of indoor localization techniques developed for real-time monitoring of older adults. These were developed within the framework of the i-Light research project that was funded by the European Union. The project targeted the development and initial evaluation of a configurable and cost-effective cyber-physical system for monitoring the safety of older adults who are living in their own homes. Localization hardware consists of a number of custom-developed devices that replace existing luminaires. In addition to lighting capabilities, they measure the strength of a Bluetooth Low Energy signal emitted by a wearable device on the user. Readings are recorded in real time and sent to a software server for analysis. We present a comparative evaluation of the accuracy achieved by several server-side algorithms, including Kalman filtering, a look-back heuristic as well as a neural networkbased approach. It is known that approaches based on measuring signal strength are sensitive to the placement of walls, construction materials used, the presence of doors as well as existing furniture. As such, we evaluate the proposed approaches in two separate locations having distinct building characteristics. We show that the proposed techniques improve the accuracy of localization. As the final step, we evaluate our results against comparable existing approaches.

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Section: Methodsmentioning

confidence: 88%

Indoor Localization Techniques Within a Home Monitoring Platform

Marin¹,

Bocicor²,

Molnar³

2020

Communications in Computer and Information Science

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“…[4][5][6][7][8][9]. However, these macrocell models results do not differ from microcells models (WalfishIkegami, ECC 33, Two slope, etc.)…”

Section: Related Workmentioning

confidence: 99%

Mobile Signal Path Losses in Microcells behind Buildings

Japertas¹,

Grimaila²

2017

RADIOENGINEERING

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Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model

Chimaobi¹,

Nnadi²,

Nkwocha³

2017

COM

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Abstract:Comparative study of two least square methods for tuning CCIR pathloss model is presented. The first model tuning approach is implemented by the addition or subtraction of the root mean square error (RMSE) based on whether the sum of errors is positive or negative. The second method is implemented by addition of a composition function of the residue to the original CCIR model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 1800 MHz frequency band. The results show that the untuned CCIR model has a root mean square error (RMSE) of 17.33 dB and prediction accuracy of 85.33%. On the other hand, the pathloss predicted by the RMSE tuned CCIR model has RMSE of 4.09dB and prediction accuracy of 96.82% while the pathloss predicted by the composition function tuned CCIR model has RME of 2.15 dB and prediction accuracy of 98.39%. In all, both methods are effective in minimizing the error to within the acceptable value of less than 7 dB. However, the composition function approach has better pathloss prediction performance with smaller RMSE and higher prediction accuracy than the RMSE-based approach.

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Empirical Characterization of Propagation Path Loss and Performance Evaluation for Co-Site Urban Environment

Cited by 5 publications

References 4 publications

Indoor Localization Techniques Within a Home Monitoring Platform

Indoor Localization Techniques Within a Home Monitoring Platform

Mobile Signal Path Losses in Microcells behind Buildings

Comparative Study of Least Square Methods for Tuning CCIR Pathloss Model

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