The study used a hedonic pricing model to examine the effect of location and neighborhood characteristics on residential rental income in Ilorin metropolis, to develop a model that analyzes the impact of location and neighbourhood on housing rental income in Ilorin metropolis. It collected field data using a standardized closed-ended questionnaire that was distributed across the Ilorin metropolis using stratified simple random. The data were examined using a descriptive statistics table and simple random sampling utilizing a statistical tool for sciences (SPSS 22). A total of 200 surveys were distributed, with 126 valid questionnaires used for the study. According to the study results, the neighbourhood features (market, educational facilities, access road, and communication facilities) are in good shape. The results also suggest that residential rental properties are positioned quite close to places of worship, markets, transit facilities, access roads, and workplaces in terms of locational qualities. According to the data, most of the physical features of residential rental properties (floor, room size, ceiling, roofing, ventilation, doors, walls, toilet facilities, electric appliances, finishing, and kitchen) are in excellent condition. According to the findings, the Taiwo neighbourhood commands the highest rent, followed by Muritata, Unity Road, and G R A. The research region's rental value variance was influenced by neighbourhood amenity and proximity to the economic area, and physical features (internal). According to the findings, neighbourhood facilities should be supplied, and existing ones should be upgraded. Investors should concentrate their efforts in areas near economic hubs. Finally, property investors should consider physical attributes critical when investing.
- Page(s): 01-09
- Date of Publication: 25 April 2022
- Uwaezuoke, Ngozi Ifeanyi Department of Estate Management and Valuation, Kwara State Polytechnic, Ilorin, Kwara State, Nigeria
- Sani, Gambo Sani Department of Estate Management and Valuation, Federal Polytechnic, Bauchi, Bauchi State, Nigeria
- Igoche, Frederick Omachoko Department of Estate Management and Valuation, Federal Polytechnic, Bauchi, Bauchi State, Nigeria
- Akaehomhen, Okoeguale Natty Department of Estate Management and Valuation, Faculty of Environmental Technology, Abubakar Tafawa Balewa University, Bauchi, Nigeria
- Sakariyau, Jamiu Kayode Department of Estate Management and Valuation, Faculty of Environmental Technology, Abubakar Tafawa Balewa University, Bauchi, Nigeria
References
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Society & Natural Resources, 28(11), 1168-1186 [7] Bourassa, S.C., Hoesli, M. and Peng, V.S. (2003), Do housing submarkets really matter, Journal of Housing Economics, Vol. 12 No. 1, pp. 12-28. [8] Bruce, R. W., & Sundell, D. J. (1977). Multiple regression analysis: history and applications in the appraisal profession. Real Estate Appraiser, 43(1), 37-44 [9] Chau, K. W., & Chin, T. L. (2003). A critical review of literature on the hedonic price model. International Journal for Housing Science and Its Applications, 27(2), 145-165 [10] Chatfield, C., & Collins, A. J. (2018). Introduction to multivariate analysis. Routledge. [11] Colwell, P. F., &Dilmore, G. (1999). Who was first? An examination of an early hedonic study. Land Economics, 620-626 [12] AT Court. (1939). Hedonic price indexes with automotive examples [13] Doling, J., & Ronald, R. (2010). Property-based welfare and European homeowners: how would housing perform as a pension?. Journal of Housing and the Built Environment, 25(2), 227-241 [14] Ezennia, I. S., & Hoskara, S. O. (2019). Exploring the severity of factors influencing sustainable affordable housing choice: evidence from Abuja, Nigeria. Sustainability, 11(20), 5792 [15] Freeman, A. M. (1981). Hedonic prices, property values and measuring environmental benefits: a survey of the issues. In Measurement in public choice (pp. 13-32). Palgrave Macmillan, London [16] Haas, G. C. (1922). A statistical analysis of farm sales in blue earth county, Minnesota, as a basis for farm land appraisal (No. 1693-2016-137481) [17] Kayode, S. J., Muhammad, M. S., & Bello, M. U. (2021). Effect of Socio-Economic Characteristics of Households on Housing Condition in Bauchi Metropolis, Bauchi State, Nigeria. TraektoriâNauki= Path of Science, 7(7), 2001-2013 [18] Manaugh, K., Miranda-Moreno, L. F., & El-Geneidy, A. M. (2010). The effect of neighbourhood characteristics, accessibility, home–work location, and demographics on commuting distances. Transportation, 37(4), 627-646. [19] Marco, L. (2008), Determinants of New York city residential rental prices The Michigan Journalof Business, Vol. 1 No. 1, pp. 61-83. [20] Monson, M. (2009). Valuation using hedonic pricing models. [21] Mooya, M. M. (2016). Neoclassical Economic Theory and Traditional Valuation Methods. Real Estate Valuation Theory, 43-63 [22] Musa, U. (2016). Impact of housing attributes on rental values of residential properties in minna, Nigeria (Doctoral dissertation, Universiti Tun Hussein Onn Malaysia) [23] Musa, H. A., Bello, M. U., & Kayode, S. J. (2021). Effect of Neighbourhood Characteristics on Resident's Satisfaction in Doya Area of Bauchi Metropolis. TraektoriâNauki= Path of Science, 7(4), 6001-6005. [24] Musa, U., Zahari, W., &Yusoff, W. (2009). The influence of housing components on prices of residential houses: A review of literature. The Social Sciences, 12(4), 625-632. [25] Muth, R. F. (1966). Household production and consumer demand functions. Econometrica: Journal of the Econometric Society, 699-708 [26] National Bureau of Statistics. (2014). Nigeria's revised and final GDP rebasing results by output approach. [27] Oates, W. E. (1969). The effects of property taxes and local public spending on property values: An empirical study of tax capitalization and the Tiebout hypothesis. Journal of political economy, 77(6), 957-971 [28] Panduro, T. E., &Veie, K. L. (2013). Classification and valuation of urban green spaces—A hedonic house price valuation. Landscape and Urban planning, 120, 119-128 [29] Redfearn C.L. (2009), How informative are average effects? Hedonic regression and amenity capitalization in complex urban housing market Regional Science and Urban Economics, Vol. 39, pp. 297-306. [30] Ridker, R. G., & Henning, J. A. (1967). The determinants of residential property values with special reference to air pollution. The review of Economics and Statistics, 246-257 [31] Roseland, M. (2012). Toward sustainable communities: Solutions for citizens and their governments. New Society Publishers [32] Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of political economy, 82(1), 34-55. [33] Sakariyau, J. K., Ajibade K, R., Muhammad, M. M., Hafsatu, J. B., Joab, S. K., & Yusuf G.L., (2020 An Analysis of Trends in Residential Property Value in Gwallameji, Bauchi, Bauchi State, Nigeria International Journal of Management Science and Entrepreneurship, 11(7), 104-135, [34] Sakariyau, J. K., Uwaezuoke, N. I,,Olaoye, T K.,& Sani, G.S (2021). Housing affordability among civil servants in Ekiti state, Nigeria. International Journal of Research and Review. 2021; 8(10): 383-390. DOI: https://doi.org/10. 52403/ijrr.20211051 [35] Scarrett, D. (2008), Property Valuation: The Five Methods, 2nd ed., Routledge, Oxford. [36] Song, Y., &Zenou, Y. (2012). Urban villages and housing values in China. Regional Science and Urban Economics, 42(3), 495-505 [37] Teck‐Hong, T. (2011). Neighborhood preferences of house buyers: the case of Klang Valley, Malaysia. International Journal of Housing Markets and Analysis [38] Thanasi, M. (2012), Improvement of service-delivery in Immovable Property RegistrationOrganization in Albaniapaper presented in ABSCR Conference, Venice, Italy, 20-22December 2012. [39] Wallace, H. A. (1926). Comparative farmland values in Iowa. The Journal of Land & Public Utility Economics, 2(4), 385-392 [40] Wickramaarachchi, N. (2015). Determinants of rental value for residential properties; A land owner's perspective for boarding homes [41] Won, J., & Lee, J. S. (2018). Investigating How the rents of small urban houses are determined: Using spatial hedonic modeling for urban residential housing in Seoul. Sustainability, 10(1), 31. [42] Wu, Y., Wei, Y. D., & Li, H. (2020). Analyzing spatial heterogeneity of housing prices using large datasets. Applied Spatial Analysis and Policy, 13(1), 223-256 [43] Xiao, Y. (2017). Hedonic housing price theory review. In Urban morphology and housing market (pp. 11-40). Springer, Singapore. [44] Yusuf, R., Muhammad, I. H., Otunola, A. O., & Kayode, S. J. (2021). Effect of Accessibility on Commercial Property Rental Values Performance in Oja Oba Market, Ilorin Metropolis. Path of Science, 7(12), 2001-2006 [45] Zhang, L., & Yi, Y. (2018). What contributes to the rising house prices in Beijing? A decomposition approach. Journal of Housing Economics, 41, 72-84.
Uwaezuoke, Ngozi Ifeanyi, Sani, Gambo Sani, Igoche, Frederick Omachoko, Akaehomhen, Okoeguale Natty, Sakariyau, Jamiu Kayode, "Hedonic Modelling of Residential Rental Values in Ilorin Metropolis" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.11 issue 4, April 2022, pp.01-09 URL: https://ijltemas.in/DigitalLibrary/Vol.11Issue4/01-09.pdf
Forest cover change can be occurred both by combining with natural influence and also anthropogenic activities. National parks can be identified as unique and the protected area for the fauna and flora species. Both natural and anthropogenic factors may have negative impact on floral and faunal populations in the national parks of Sri Lanka. Therefore, identification of the changes and conservation of biodiversity of national parks is timely valuable. GIS is a more effective tool for identifying the change detection of forest cover. The aim of this study was to explore the spatial and temporal changes of forest cover in Minneriya National Park between 2000-2014. This research is mainly based on secondary data of land use maps especially the maps available for 2000 & 2014. The maps can be created by adopting classification and digitization. By overlaying these two maps it could be identified the changes of forest cover within the fourteen-year period from 2000 to 2014. It was possible from this exercise to observe the spatial and temporal changes of the forest area in the park. In the year 2000 forest cover was 42.67 km2 and it was 36.65 km2 in 2014 showing a reduction of 14%. The Scrub area has increased. In 2000 it was 21.2 km2 and in 2014 it came up to 27.83km2 showing an increase of 31%. In the year 2000 tank area was 22.89 km2, which was reduced down to 22.37 km2 by 2014. The real reasons for these changes need to be examined using field studies and impact on terrestrial and aquatic biodiversity should be considered carefully as the land area is devoted to a National Park of Sri Lanka.
- Page(s): 10-14
- Date of Publication: 25 April 2022
- H.U.K Dilanjani Faculty of Graduate Studies, University of Sri Jayawardhanepura, Nugegoda, Sri Lanka
- R.M.K Ratnayake Faculty of Graduate Studies, University of Sri Jayawardhanepura, Nugegoda, Sri Lanka
- P.B Dharmasena Faculty of Graduate Studies, University of Sri Jayawardhanepura, Nugegoda, Sri Lanka
References
[1] Sonti SH (2015) Application of Geographic Information System (GIS) in Forest Management. J Geogr Nat Disast 5: 145. doi:10.4172/2167-0587.1000145
[2] Redlands, E. S. (1999). Getting to Know Arc View GIS for everyone. ESRI.
[3] Devendra Kumar , (2011). Monitoring Forest Cover Changes Using Remote Sensing and GIS: A Global Prospective. Research Journal of Environmental Sciences, 5: 105-123.
[4] Green, M.J.B. (ed.), De Alwis, S.M.D.A.U., Dayawansa, P.N., How, R., Padmalal, U.K.G.K., Singhakumara, B.M.P., Weerakoon, D. and Wijesinghe, M.R. Infotechs IDEAS in association with GREENTECH Consultants. (2008). Sri Lanka Protected Areas Management and Wildlife Conservation Project (PAM&WCP/CONSULT/02/BDBS), Department of Wildlife Conservation, Ministry of Environment and Natural Resources, Colombo. 47 pp.[http://203.143.23.34/BBS/bbs.html],Department of Wildlife Conservation Ministry of Environment and Natural Resources, S. L. Biodiversity baseline survey:minneriya national park.
[5] Department of Wildlife Conservation Ministry of Environment and Natural Resources, Sri Lanka.
[6] Adia, S. O., and Rabiu, A. B. Change Detection of Vegetation Cover, using Multi-temporal Remote Sensing Data and GIS Techniques, 2007. Available at http://www.gisdevelopment.net/application/envi
[7] Alagan, R. (2009). Sri Lanka’s Forest Cover: What We Know and What we Don’t . Sri Lanka.
[8] Arino, O., Bicheron, P., Achard, F., Latham, J., Witt, R. & Weber, J.L. (2008). The most detailed portrait of Earth. ESA Bulletin (European Space Agency) (DepartmDepartment of Wildlife Conservation Ministry of Environment and Natural Resources, 2008)
[9] Erik J. lindquist, r. d. (2012). Global forest land-use change 1990-2005. Rome, Italy: Food And Agricultural Organization.
[10] Forkuo, E. K. (December 2012). Analysis of Forest Cover Change Detection. Ghana: Department of Geomatic Engineering, Kwame Nkrumah University of Science & Technology.https://scialert.net/abstract/?doi=rjes.2011.105.123
[11] J.S. Rawat, M. K. (2014). Monitoring land use/cover change using remote sensing & GIS . India: Department of Geography, Kumaun University, SSJ Campus, Almora 263601, India.
[12] Nations, F. A. (Rome, 2010). Global Forest Resources assessment 2010. Rome: food and agriculture organization of the united nations.
[13] Oxford, U. O. (September, 2013). Land Cover Change Analysis in Tropical Forest Ecosystems Using GIS ,University of Oxford.
[14] Senarathne, P. (2004). National Parks of Sri Lanka. Sikuru Publisher.
[15] Wachiye, S. A. ( 2013). GIS-based forest cover change and vulnerability. Journal of Geography and Regional Planning.
[16] Wildlife Sanctuaries of Sri Lanka, Department of Wildlife Conservation, Colombo 7
[17] Yude Pan, R. A. (2013). The Structure, Distribution, and Biomass of the World’s forest. Pennsylvania: U.S. Department of Agriculture.
H.U.K Dilanjani, R.M.K Ratnayake, P.B Dharmasena, "Spatial and Temporal Changes of Forest Cover and Tank System in Dry Zone of Sri Lanka: A Case Study from Minneriya National Park" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.11 issue 4, April 2022, pp.10-14 URL: https://ijltemas.in/DigitalLibrary/Vol.11Issue4/10-14.pdf
Heart abnormality refers to the irregular electrical activity of the heart. Heart abnormality sometimes does not show any symptom and it is not sensible, and this may cause the heart to cease and stop functioning and thereby lead to sudden death. This paper attempts to develop a program that capable to detect the heart abnormality activity through the implementation of spectral analysis using SPSS. A dataset of heartbeat signals from electrocardiogram (ECG) from Heart-Statlog.arff will be used to test the network performance.
- Page(s): 15-19
- Date of Publication: 30 April 2022
- Dr. Egba Ifeanyichukwu (B.Tech, M.Sc, IITP, Ph.D),Computer Department, Rivers State College of Health Science and Technology, Rumueme, Port Harcourt. Nigeria
- Dike Blessed O.P (PhD) (BSc, M.Sc, Ph.D),Medical Imaging Technology Department, Rivers State College of Health Science and Technology, Rumueme, Port Harcourt. Nigeria.
- Akiene Nunyor (BSc) (BSc), Medical Imaging Technology Department, Rivers State College of Health Science and Management Technology, Rumueme, Port Harcourt. Rivers State, Nigeria
References
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Dr. Egba Ifeanyichukwu, Dike Blessed O.P (PhD), Akiene Nunyor (BSc), "The Analysis of Electrocardiogram signal processing showing nonlinear structure (abnormalities) of the heart using the Spectrum Analysis in SPSS" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.11 issue 4, April 2022, pp.15-19 URL: https://ijltemas.in/DigitalLibrary/Vol.11Issue4/15-19.pdf
This study focuses on the validation of load profile and energy usage of Itie-Ikpe, a rural community located in the southern part of Nigeria, for the purpose of designing sustainable optimal hybrid renewable energy systems. A two-year monthly estimated load profile data was obtained from an energy distribution company, monthly averages and overall annual daily load demand of 557.59 kWh/d were determined using mathematical method while a field survey was conducted to obtain the actual load demand figure 562.25 kWh/d for the study community with a difference of 4.66 kWh/d. The two independent results were comparatively analyzed with the field survey result higher than the estimated value obtained from the energy distribution company by 0.84%. Findings also showed that the error margin or difference was relatively negligible with little or no significant effect on the design of the optimal hybrid renewable energy system. Therefore, the daily load demand figure from the field survey result validated the average daily load demand value determined from the obtained estimates. This validation is a crucial step in designing an optimal hybrid renewable energy system for energy sustainability of the study community, as the modeling and selection of systems components of the hybrid renewable energy system depends on the knowledge of accurate average daily load demand in order to avoid design of ineffective energy systems whose performances might not be optimal.
- Kehinde Samuel Owoeye Department of Electrical/Electronic and Computer Engineering, University of Uyo, Uyo, Nigeria
- Kufre Michael Udofia Department of Electrical/Electronic and Computer Engineering, University of Uyo, Uyo, Nigeria
- Nseobong Ibanga Okpura Department of Electrical/Electronic and Computer Engineering, University of Uyo, Uyo, Nigeria
- Abiodun Ezekiel Abiola Department of Computer Engineering, Federal University, Oye-Ekiti, Ekiti State, Nigeria
References
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(2019). 2nd International Conference on Sustainable Materials Processing and Manufacturing (SMPM 2019). Procedia Manufacturing 35 (2019) 278–284. [11] Ohunakin O. S., Adaramola M. S., Oyewola O. M., and Fagbenle R. O. (2014). Solar energy applications and development in Nigeria: Drivers and barriers. Renew. Sustain. Energy Rev., vol. 32, pp. 294–301. [12] Oladigbolu J. O. (2019). Optimal Configuration and Economic Assessment of a Hybrid PV/diesel Energy System for Remote Rural healthcare load: An Approach Towards Rural Development, Int. J. Sci. Eng. Res., vol. 10, no. 8, pp. 1309–1313. [13] Oladigbolu J. O. (2020). Economic Evaluation and Determination of Optimal Hybrid Energy Supply Systems for Residential and Healthcare Facilities in Rural and Urban Areas, King Abdulaziz Univ. Sci. Publ. Cent., no. January, 2020. [14] Oladigbolu Jamiu O., Ramli Makbul A. M., and Al-turki Yusuf. A. (2020). Feasibility Study and comparative analysis of hybrid renewable power system for off-grid rural electrification in a typical remote village located in Nigeria. IEEE Access. [15] Oladigbolu J. O., Ramli M. A. M., and Al-turki Y. A. (2019). Techno-Economic and Sensitivity Analyses for an Optimal Hybrid Power System Which Is Adaptable and Effective for Rural Electrification: A Case Study of Nigeria. Sustain 2019. [16] Olatomiwa L., Blanchard R., Mekhilef S., and Akinyele D. (2018). Hybrid renewable energy supply for rural healthcare facilities: An approach to quality healthcare delivery. Sustain. Energy Technol. Assessments, vol. 30, no. February, pp. 121–138. [17] Olatomiwa L., Mekhilef S., Huda A. S. N., and Ohunakin O. S. (2015). Economic evaluation of hybrid energy systems for rural electrification in six geo-political zones of Nigeria. Renew. Energy, vol. 83, pp. 435–446. [18] Owoeye, Kehinde S., Okpura, Nseobong I., Udofia Kufre M., (2022). Sensitivity Analysis of an Optimal Hybrid Renewable Energy System for Sustainable Power Supply to a Remote Rural Community. International Journal of Advances in Engineering and Management, 2022. [19] Owoeye Kehinde S., Udofia Kufre M., Okpura Nseobong I., (2022). Design and Optimization of Hybrid Renewable Energy System for Rural Electrification of an Off-grid Community. European Journal of Engineering and Technology 2022. [20] Oyedepo, S.O., Babalola, O.P., Nwanya, S.C., Kilanko, O., Leramo, R.O., Aworinde, A.K., Adekeye, T., Oyebanji, J.A., Abidakun, A.O., Agberegha, O.L. (2018). Towards a Sustainable Electricity Supply in Nigeria: The Role of Decentralized Renewable Energy System. Eur. J. Sustain. Dev. Res. 2018, 2, 40. [21] PHED, Port Harcourt Electricity Distribution Company (2021). Port Harcourt, Nigeria. [22] Samir M. Dawoud. (2021). Developing different hybrid renewable sources of residential loads as a reliable method to realize energy sustainability. 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Kehinde Samuel Owoeye, Kufre Michael Udofia, Nseobong Ibanga Okpura, Abiodun Ezekiel Abiola, "Load Profile Validation for Design of Sustainable Optimal Hybrid Renewable Energy Systems" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.11 issue 4, April 2022, pp.20-26 https://dx.doi.org/10.51583/IJLTEMAS.2022.11401
A material description of oil palm empty fruit bunch fibre and high density polyethylene (OPEFB-HDPE) composites for microwave shielding applications was established by determining it reflection (S11) and transmission (S21) coefficient with respect to percentage and frequency. The proposed OPEFB-HDPE composites was studied at frequency from 8-12 GHz. The study was conducted using Waveguide Agilent N5230A PNA and Comsol software technique. The effect of different percentages (10%OPEFB-90%HDPE, 20%OPEFB-80%HDPE, 30%OPEFB-70%HDPE, 40% OPEFB-60%HDPE, 50%OPEFB -50%HDPE) and frequency (8-12 GHz) on S11 and S21 of OPEFB-HDPE composites were investigated. Results showed that the percentage of OPEFB significantly influenced the reflection S11 and transmission S21 coefficient of OPEFB-HDPE composites. Furthermore, the reflection S11 coefficient increased in magnitude with increased in OPEFB percentage and decreased with increased in frequency while the transmission S21 coefficient decreased in magnitude with increased in OPEFB percentage and increased with increased in frequency. The S11 and S21 of the smallest and largest percentage (10 and 50%) of OPEFB for RWG at smaller and bigger frequencies (8 and 12 GHz) were (0.6298 and 0.7912) and (0.7157 and 0.5699), the S11 of the smallest and largest percentage (10 and 50%) of OPEFB for FEM at smaller and bigger frequencies (8 and 12 GHz) were (0.6128 and 0.8217) and (0.8217 and 0.5673) respectively. The higher percentage of OPEFB filler the higher the reflection S11 coefficient, and the lower the transmission S21 coefficient. Also the higher percentage of HDPE host matrix the higher the transmission S21 coefficient and the lower the reflection S11 coefficient.
- Page(s): 27-34
- Date of Publication: 14 May 2022
- Abubakar Dantani Meli Department of Science Education, Waziri Umaru Federal Polytechnic Birnin Kebbi, Nigeria
- CHE Azurahanim Che Abdullah Material Synthesis and Characterization Lab, Institute of Advanced Technology, University Putra Malaysia 43400 Serdang Selangor
- Usman Atiku Kende Department Science Laboratory technology, Waziri Umaru Federal Polytechnic Birnin Kebbi, Nigeria
- Abubakar Samaila Department of Science Education, Waziri Umaru Federal Polytechnic Birnin Kebbi, Nigeria
- Shehu Umar Department of Science Education, Waziri Umaru Federal Polytechnic Birnin Kebbi, Nigeria
- Muhammad Aliyu Wara Department of remedial studies, Waziri Umaru federal Polytechnic Birnin Kebbi, Nigeria
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Abubakar Dantani Meli, CHE Azurahanim Che Abdullah, Usman Atiku Kende, Abubakar Samaila, Shehu Umar, Muhammad Aliyu Wara, "Measured and Simulated Scattering Parameters Analysis of Oil Palm Empty Fruit Bunch fibre-High Density Polyethylene Composites, using Rectangular Waveguide and Finite Element Method for Shielding Application" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.11 issue 4, April 2022, pp.27-34 URL: https://ijltemas.in/DigitalLibrary/Vol.11Issue4/27-34.pdf
This paper proposes an ALOHA based Dynamic Framed Slotted ALOHA algorithms using analytical procedure to estimate the number of tags within the radio range of the reader, and Time Division Multiple access (TDFSA) to dynamically allocate number of slots per frame through which the tags send their response to the reader. The common algorithms used for anti-collision algorithm in RFID system is the Framed slotted aloha, however most of the previous algorithm suffer from optimum object identification especially when the number of tags progressively increase. It is therefore the aim of this paper to propose an algorithm that can adaptively estimate the communication channel size with variation in the number of tags. The performance of the proposed TDFSA (Time Division Framed Slotted ALOHA) is compared to DFSA (Dynamic Framed Slotted ALOHA) using simulation. Simulation results show that the proposed algorithm though increases latency compared to DFSA by 3% outperforms the DFSA in throughput by 29% especially n scenario where many tags are involved.
- Page(s): 35-41
- Date of Publication: 14 May 2022
- FAGBOHUNMI, Griffin Siji Department of Computer Engineering Abia State University, Uturu, Abia State, Nigeria
- Uchegbu Chinenye E. Department of Electrical and Electronics Engineering, Abia State University, Uturu, Abia State, Nigeria
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FAGBOHUNMI, Griffin Siji, Uchegbu Chinenye E., "An Anti-collision Algorithms for Optimum Throughput in Passive RFID Identification System" International Journal of Latest Technology in Engineering, Management & Applied Science-IJLTEMAS vol.11 issue 4, April 2022, pp.35-41 URL: https://ijltemas.in/DigitalLibrary/Vol.11Issue4/35-41.pdf