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Juan Carlos Vesga, F.
- Use of Assignment Models as a Strategy for Channel Optimization in the 5 GHz Band Supported in 802.11ac
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1 Escuela de Ciencias Basicas Tecnologia e Ingenieria (ECBTI), Universidad Nacional Abierta y a Distancia; Carrera 27 Nro. 40-43. Bucaramanga, CO
1 Escuela de Ciencias Basicas Tecnologia e Ingenieria (ECBTI), Universidad Nacional Abierta y a Distancia; Carrera 27 Nro. 40-43. Bucaramanga, CO
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Indian Journal of Science and Technology, Vol 11, No 22 (2018), Pagination: 1-17Abstract
Objectives: The growing demand of wireless connectivity supported in the standard 802.11 has brought high levels of interference between the adjoining Access Points (APs), due to the shared use of the ISM bands, considerably affecting the network performance. The objective of this paper is to propose an optimization model for the allocation of channels in the band of 5 GHz, supported in the use of allocation models. Methods Analysis: A scenario formed by 6-storey building and 24 Access Points distributed within the building (AP) is proposed. The optimization model for the allocation of band frequencies of 5 GHz is represented as a problem of linear programming, based on an allocation model, which is an alternative for the transport. The model seeks to maximize the allocated bandwidth to each AP, to minimize the interference between AP, increase the SINR levels and spectral efficiency, by means of the optimal clustering of channels within the UNII range. In order to determine if the proposed model (MF) does a better allocation of channels than the current model (MA), which incorporates RRM policies? A hypothesis contrast was carried out under difference of means for two independent samples, by means of the use of T-students test. Findings: Even though several works have been done related to the channel allocation, there was no evidence of any optimization model oriented to the band of 5 GHz and that is why the proposed model could be considered as the first optimization model for this frequency band. Based on the obtained results, it was possible to observe that the proposed optimization model (MF) did a better allocation of spectral resources compared to the current model (MA), being able to increase in more than 50% in the average SINR with a 95% confidence. Application: The model MF can be considered as a tool in future works of research, related to the design and analysis of wireless networks that use 5 GHz bands, in order to assess the performance, efficiency and QOS aspects.References
- Zvanovec S, Pechac P, Klepal M. Wireless LAN networks design: Site survey or propagation modeling?Radioengineering. 2017. p. 1–8.
- Yeong SY, Al-Salihy W, Wan TC. Indoor WLAN monitoring and planning using empirical and theoretical propagation models. Second International Conference on Network Applications Protocols and Services; 2010. p. 165–9.
- Tramarin F, Vitturi S, Luvisotto M, Zanella A. On the use of IEEE 802.11n. Information Technology, Computer, and Electrical Engineering (ICITACEE). 2016; 12(5):1877–86.
- Syafei WA. Implementation of K-Best method for MIMO decoder in WLAN 802.11n. 2nd International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE); 2015. p. 417–21.
- Haidar M, Ghimire R, Al-Rizzo H, Akl R, Yupo Chan. Channel assignment in an IEEE 802.11 WLAN based on Signal-to-Interference Ratio. Canadian Conference on Electrical and Computer Engineering; 2008. p. 1169–74.
- Gong D, Yang Y. Link-Layer multicast in large-scale 802.11n wireless LANs with smart antennas. IEEE Transaction Computer. 2016; 65(7):2118–33.
- Soleymani M, Maham B, Ashtiani F. Analysis of the downlink saturation throughput of an asymmetric IEEE 802.11n-based WLAN. IEEE International Conference on Communications (ICC); 2016. p. 1–6.
- Romero G, Simon EP, Deniau V, Gransart C, Kousri M. Evaluation of an IEEE 802.11n communication system in presence of transient electromagnetic interferences from the pantograph-catenary contact. 2017 XXXIInd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS); 2017. p. 1–4.
- Solahuddin YF, Mardeni R. Indoor empirical path loss prediction model for 2.4 GHz 802.11n network. IEEE International Conference on Control System, Computing and Engineering; 2011. p. 12–7.
- Rajesh A, Pragathi G, Shankar T. Investigation of an improved adaptive power saving technique for IEEE 802.11ac systems. Indian Journal of Science and Technology. 2016; 9(37):1–7.
- Kuntal A, Karmakar P, Chakraborty S. Optimization technique based localization in IEEE 802.11 WLAN. International Conference on Recent Advances and Innovations in Engineering (ICRAIE); 2014. p. 1–5.
- Basha Pathan H, Varma PS, Rajesh KS. QOS performance of IEEE 802.11 in MAC and PHY layer using enhanced OAR algorithm. Indian Journal of Science and Technology. 2017; 10(9):1–9.
- Bahs A, Matsui M, Asai Y, Mizoguchi M. Network controlled frequency channel and bandwidth allocation scheme for IEEE 802.11a/n/ac wireless LANs: RATOP. IEEE International Symposium on Personal Indoor Mob Radio Common PIMRC; 2015 Jun. p. 1041–5.
- Haidar M, Akl R, Al-Rizzo H, Chan Y. Channel assignment and load distribution in a power-managed WLAN. Annual International Symposium on Personal, Indoor and Mobile Radio Communication (PIMRC); 2007. p. 1041–5.
- Desimone R, Brito BM, Baston J. Model of indoor signal propagation using log-normal shadowing. Long Island Systems, Applications and Technology; 2015. p. 1–4.
- Vanhatupa T, Hannikainen M, Hamalainen TD. Genetic algorithm to optimize node placement and configuration for WLAN planning. 4th International Symposium on Wireless Communication Systems; 2007. p. 612–6.
- Sangolli SV, Jayavignesh T. TCP throughput measurement and comparison of IEEE 802.11 legacy, IEEE 802.11n and IEEE 802.11ac Standards. Indian Journal Science and Technology. 2015; 8(20):1–8.
- Sivakumar P, Vinod B, Sandhya Devi RS, Jayasakthi Rajkumar ER. Real-time task scheduling for distributed embedded system using MATLAB toolboxes. Indian Journal of Science and Technology. 2015; 8(15):1–7.
- Ravindranath NS, Singh I, Prasad A, Rao VS. Performance evaluation of IEEE 802.11ac and 802.11n using NS3. Indian Journal Science Technology. 2016; 9(26):1–9.
- Franco JR, Rodríguez AIP, Jimeenez REC. Estadiistica Aplicada. II, Estadiistica En Administracioon Para La Toma de Decisiones; 2014. p. 1–34.
- Design of Empirical Propagation Models Supported in the Log-Normal Shadowing Model for the 2.4 GHz and 5 GHz Bands Under Indoor Environments
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Authors
Affiliations
1 Escuela de Ciencias Basicas Tecnologia e Ingenieria (ECBTI), Universidad Nacional Abierta y a Distancia; Carrera 27 Nro. 40-43. Bucaramanga, CO
2 Facultad de Ingenieria, Corporacion Universitaria de Ciencia y Desarrollo; Cra. 12 #37-14. Bucaramanga, CO
1 Escuela de Ciencias Basicas Tecnologia e Ingenieria (ECBTI), Universidad Nacional Abierta y a Distancia; Carrera 27 Nro. 40-43. Bucaramanga, CO
2 Facultad de Ingenieria, Corporacion Universitaria de Ciencia y Desarrollo; Cra. 12 #37-14. Bucaramanga, CO
Source
Indian Journal of Science and Technology, Vol 11, No 22 (2018), Pagination: 1-18Abstract
Objectives: The growing demand of Wireless connectivity supported in the 802.11 standard has caused a high demand of wireless networks by the users, due to the benefits of mobility and low-cost implementation. Such networks have been used in common places such as houses, offices, schools among others. The objective of this article is to propose an empiric propagation model, supported in the Log-Normal-Shadowing Path Loss model for 2.4 GHz and 5 GHz bands in Indoor environments, compatible with University campus building. Methods/Statistical Analysis: A scenario of a six-storey building on a university campus was proposed, with dimensions of 60 m long, 34 m wide and 24 m high. Additionally, such building has 24 Access Points (AP) distributed in such a way that allow network to serve connectivity to its students. In order to establish the expressions that describe the Log-Normal-Shadowing Path Loss propagation model, an experimental design of factorial mixed type was considered involving three factors: frequency band (2.4 GHz or 5 GHz), condition of the media (Free space or with obstacles) and the distance between the receiver and the AP. The experiment aims to assess how each factor influences on the Received Signal Strength Indicator RSSI. Topic Significance: The proposed model will be used to run analysis of radio propagation in indoor environments using institutional educational buildings and holding a capacity of carrying out prediction processes of RSSI, in the 2.4 GHz and 5 GHz bands. Findings: Based on obtained results, it was possible to evidence that the established models allow predicting the behavior the reception power and signal damping, with a level of confidence of 95%. Additionally, it was possible to verify, in the 5 GHZ band, the damping levels are bigger that those in 2.4 GHz band. Such aspect holds great importance when it comes to perform design processes of Wireless networks. Applications/Improvements: In future research papers, it would be significantly important to establish the expression for outdoor environments, supported in the Shadowing Path model.References
- Rajesh A, Pragathi G, Shankar T. Investigation of an improved adaptive power saving technique for IEEE 802.11ac Systems. Indian Journal of Science and Technology. 2016; 9(37):1–7.
- Ibhaze AE, Imoize AL, Ajose SO, John SN, Ndujiuba CU, Idachaba FE. An empirical propagation model for path loss prediction at 2100 MHz in a Dense Urban Environment. Indian Journal of Science and Technology. 2017; 10(5):1–9.
- Yeong SY, Al-Salihy W, Wan TC. Indoor WLAN monitoring and planning using empirical and theoretical propagation models. IEEE Second International Conference on Network Applications, Protocols and Services; 2010. p. 165–9.
- Basha Pathan H, Varma PS, Rajesh KS. QoS performance of IEEE 802.11 in MAC and PHY layer using enhanced OAR algorithm. Indian Journal of Science and Technology. 2017; 10(9):1–9.
- Zvanovec S, Pechac P, Klepal M. Wireless LAN networks design: Site survey or propagation modeling? Radio Engineering. 2003; 12(4):1–8.
- Zarkovic J, Stojkovic P, Neskovic N. 3D statistical propagation model for indoor WLAN radio coverage. IEEE 19thTelecommunications Forum (TELFOR); 2011. p. 461– 4.
- Sangolli SV, Jayavignesh T. TCP throughput measurement and comparison of IEEE 802.11 legacy, IEEE 802.11n and IEEE 802.11ac Standards. Indian Journal of Science and Technology. 2015; 8(20):1–8.
- Desimone R, Brito BM, Baston J. Model of indoor signal propagation using log-normal shadowing. IEEE Long Island Systems, Applications and Technology; 2015. p. 1–4.
- Solahuddin YF, Mardeni R. Indoor empirical path loss prediction model for 2.4 GHz 802.11n network. IEEE International Conference on Control System, Computing and Engineering; 2011. p. 12–7.
- Li L, Ibdah Y, Ding Y, Eghbali H, Muhaidat SH, Ma X. Indoor multi-wall path loss model at 1.93 GHz. IEEE MILCOM 2013-2013 Military Communications Conference; 2013. p. 1233–7.
- Chrysikos T, Georgopoulos G, Kotsopoulos S. Attenuation over distance for indoor propagation topologies at 2.4 GHz. IEEE Symposium on Computers and Communications (ISCC); 2011. p. 329–34.
- Chrysikos T, Georgopoulos G, Kotsopoulos S. Wireless channel characterization for a home indoor propagation topology at 2.4 GHz. IEEE Wireless Telecommunications Symposium (WTS); 2011. p. 1–10.
- Andrade CB, Hoefel RF, Cheung KW. IEEE 802. 11 WLANS: A comparison on indoor coverage models. Canadian Conference on Electrical and Computer Engineering; 2010. p. 1–10.
- Kuntal A, Karmakar P, Chakraborty S. Optimization technique based localization in IEEE 802.11 WLAN. International Conference on Recent Advances and Innovations in Engineering; 2014. p. 1–5.
- Salazar JC, Zapata AB. Analisis y diseno de experimentos aplicados a estudios de simulacion analysis and design of experiments applied to simulation studies. 2009; 76(159):249–57.
- Del Ca-izo Lopez JF, Lopez Martín D, Lledo Garcia E GBP. Dise-o de modelos experimentalesen investigación quirúrgica. Actas Urologicas Espa-olas. 2008; 32(1):27–40.
- Ravindranath NS, Singh I, Prasad A, Rao VS. Performance Evaluation of IEEE 802.11ac and 802.11n using NS3. Indian Journal of Science and Technology. 2016; 9(26):1–9.
- Diaz Cadavid A. Diseno Estadistico de Experimentos. 2nd Ed. Editorial Universidad de Antioquia. 2009. p. 1–286.
- Gonzaalez Manteiga MT, Peerez de Vargas A. Estadística Aplicada Una Visioon Instrumental. Ediciones Diaz de Santos; 2010.
- Rodriiguez FJ, Pierdant Rodríguez AI, Rodriiguez Jimeenez EC. Estadiistica Aplicada. II, Estadiistica En Administracioon Para La Toma de Decisiones; 2014. p. 1–34.
- Optimization of the Spectral Efficiency in WLAN Networks in the 2.4GHz Band Under the Use of Allocation Models
Abstract Views :194 |
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Authors
Affiliations
1 Escuela de Ciencias Básicas Tecnología e Ingeniería (ECBTI), Universidad Nacional Abierta y a Distancia; Carrera 27 Nro. 40-43. Bucaramanga, CO
1 Escuela de Ciencias Básicas Tecnología e Ingeniería (ECBTI), Universidad Nacional Abierta y a Distancia; Carrera 27 Nro. 40-43. Bucaramanga, CO
Source
Indian Journal of Science and Technology, Vol 11, No 22 (2018), Pagination: 1-13Abstract
Background/Objectives: As a result of the proliferation of Access Point (AP) in various environments, it has demonstrated a significant increase in the interference levels betweenadjacent AP Due to sharing of the ISM bands, significantly affecting network performance.The aim of this paper is to propose an optimization model for allocating channels in the 2.4GHz band, supported in the use allocation models. Methods/Statistical Analysis: A scenario consisting of 6 storey building and 24 Access Point (AP) distributed inside was proposed. The optimization model for allocating frequencies in the 2.4 GHz band is represented as a linear programming problem based on an allocation model, which is a variant of transport models. The model seeks to minimize interference between AP and thereby increase SINR levels and spectral efficiency. To determine whether the proposed model (MF) makes a better allocation of channels than the current model (MA), which incorporates policies RRM, a hypothesis test is performed under mean difference for two independent samples, by t tests -student. Topic Relevance: Although there have been several related channel assignment work, no evidence of any optimization model that has considered using allocation models as a strategy for optimizing spectral efficiency was found. In addition, the model offers levels and reduced computational time complexity. Findings: based on the results it was evident that the model proposed optimization (MF) made a better allocation of resources in the frequency domain compared to the current model (MA), reaching an increase of about 15% in SINR average, with 95% confidence. Application / Improvements: The MF model can be considered as a tool in future research related to the design and analysis of wireless networks which use the 2.4GHz band, to assess aspects of performance, efficiency and QoS.References
- Sacoto A, Solís J, Novillo F. Algoritmo de Asignación de Canales para Redes de Comunicación Inalámbricas con Acceso Oportunista basado en Algoritmos Genéticos. Sustentación de Trabajo de Graduación, At Guayaquil, Ecuador; 2014.
- Tramarin F, Vitturi S, Luvisotto M, Zanella A. On the use of IEEE 802.11n for industrial communications. IEEE Transactions on Industrial Informatics. 2016; 12(5):1877– 86. Crossref
- Ibhaze AE, Imoize AL, Ajose SO, John SN, Ndujiuba CU, Idachaba FE. An empirical propagation model for path loss prediction at 2100MHz in a dense urban environment. Indian Journal of Science and Technology. 2017;10(5):1–9. Crossref
- Syafei WA. Implementation of K-Best method for MIMO decoder in WLAN 802.11n. 2nd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE); 2015. p. 417–21. Crossref
- Abeysekera BAHS, Matsui M, Asai Y, Mizoguchi M. Network controlled frequency channel and bandwidth allocation scheme for IEEE 802.11a/n/ac wireless LANs: RATOP. IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC); 2014. p. 1041–5.
- Yeong S-Y, Al-Salihy W, Wan T-C. Indoor WLAN monitoring and planning using empirical and theoretical propagation models. 2010 IEEE 2nd International Conference on Network Applications, Protocols and Services; 2010. p. 165–9.
- Rajesh A, Pragathi G, Shankar T. Investigation of an Improved adaptive power saving technique for IEEE 802.11ac systems. Indian Journal of Science and Technology. 2016; 9(37):1–7. Crossref
- Haidar M, Akl R, Al-Rizzo H, Chan Y. Channel assignment and load distribution in a power-managed Wlan. IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications; 2007. p. 1–5. Crossref
- Zhou K, Jia X, Xie L, Chang Y, Tang X. Channel assignment for WLAN by considering overlapping channels in SINR interference model. IEEE 2012 International Conference on Computing, Networking and Communications (ICNC); 2012. p. 1005–9. Crossref
- Abu-Tair M, Bhatti SN. Introducing IEEE 802.11ac into existing WLAN deployment scenarios. 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt); 2015. p. 30–5. Crossref
- Basha PH, Varma PS, Rajesh KS. QoSperformance of IEEE 802.11 in MAC and PHY layer using Enhanced OAR Algorithm. Indian Journal of Science and Technology. 2017; 10(9):1–9. Crossref
- Chrysikos T, Georgopoulos G, Kotsopoulos S. Attenuation over distance for indoor propagation topologies at 2.4 GHz. IEEE Symposium on Computers and Communications (ISCC); 2011. p. 329–34. Crossref
- Ravindranath NS, Singh I, Prasad A, Rao VS. Performance evaluation of IEEE 802.11ac and 802.11n using NS3. Indian Journal of Science and Technology. 2016; 9(26):1–9. Crossref
- Soleymani M, Maham B, Ashtiani F. Analysis of the downlink saturation throughput of an asymmetric IEEE 802.11n-based WLAN. IEEE International Conference on Communications (ICC); 2016. p. 1–6. Crossref
- Sangolli SV, Jayavignesh T. TCP throughput measurement and comparison of IEEE 802.11 legacy, IEEE 802.11n and IEEE 802.11ac standards. Indian Journal of Science and Technology. 2015; 8(20):1–8. Crossref
- Salazar JC, Zapata AB. Análisis y Dise-o de Experimentos Aplicados a Estudios de Simulación Analysis and Design of Experiments Applied to Simulation Studies. 2009; 159:249–57.
- Algorithms for Estimation of the Coverage Area and Low Blocking Probability Model Log-Normal Shadowing for 2.4 GHz and 5 GHz in Indoor Environments
Abstract Views :199 |
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Authors
Affiliations
1 Escuela de Ciencias Básicas Tecnología e Ingeniería (ECBTI), Universidad Nacional Abierta y a Distancia; Carrera 27 Nro. 40-43. Bucaramanga, CO
2 Universidad Pontificia Bolivariana; Km 3 vía Piedecuesta, CO
1 Escuela de Ciencias Básicas Tecnología e Ingeniería (ECBTI), Universidad Nacional Abierta y a Distancia; Carrera 27 Nro. 40-43. Bucaramanga, CO
2 Universidad Pontificia Bolivariana; Km 3 vía Piedecuesta, CO
Source
Indian Journal of Science and Technology, Vol 11, No 33 (2018), Pagination: 1-10Abstract
Background/Objectives: In designing WLAN networks is difficult to determine exactly the maximum range of the signal radiated by an Access Point, due to the random behavior of the signal received power and receiver sensitivity. The aim of this paper is to develop an algorithm estimate the probability of Court and the coverage area for an Access Point (AP) in the 2.4GHz and 5GHz bands. Methods/Statistical Analysis: For estimating the outage probability and the coverage area, two routines in Matlab for each frequency band supported on the propagation model Log-Normal Shadowing Path Loss developed, which allow decompose the received power at an average power and attenuation term shadow. Topic Relevance: Although there have been various related design WLANs work, no evidence of an algorithm to estimate the coverage area and the likelihood of court, considering it was found that, in most cases, the estimation of these parameters it is performed graphically and by using software tools on a plane set by the designer. Aspect by which developed in Matlab routines may be used in future research related to the design of WLANs. Results: Based on the results it was evident that it is possible to predict the area of coverage and outage probability for the 2.4GHz and 5GHz according to the transmission power, the detection threshold of the receiver, the probability estimated cut and environment characterization between the AP and the receiver, either free space or obstacles, supported using a shadow model attenuation. Additionally, routines allowed the generation of curves describing the behavior of area coverage and outage probability in terms of percent, depending on the radius of coverage, frequency band and environmental conditions, with 95% confidence. Application/Improvements: The developed routines can be used as support tools in future researchReferences
- Carlos VFJ, Fabiola CHM, Jose AVB. Design of empirical propagation models supported in the LogNormal Shadowing model for the 2.4GHz and 5GHz bands under Indoor environments. Indian Journal of Science and Technology. 2018; 11(22):1–18. https://doi.org/10.17485/ijst/2018/v11i20/122149
- Abeysekera BAHS, Matsui M, Asai M. Mizoguchi network controlled frequency channel and bandwidth allocation scheme for IEEE 802.11a/n/acwireless LANs: RATOP. IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC); 2014. p. 1041–5.
- Juan CVF, Martha FCH, Perez Esneider HW. Optimization of the spectral efficiency in WLAN networks in the 2.4GHz band under the use of allocation models. Indian Journal of Science and Technology. 2018; 11(22):1–13. https://doi.org/10.17485/ijst/2018/v11i22/122475
- Vesga FJCF, Contreras HMF, Perez EHW. Use of assignment models as a strategy for channel optimization in the 5GHz band supported in 802.11ac. Indian Journal of Science and Technology. 2018; 11(22):1–17. https://doi.org/10.17485/ijst/2018/v11i22/121348
- Wireless LAN networks design: Site survey or propagation modeling? Available from: https://www.radioeng.cz/fulltexts/ 2003/03_04_42_49.pdf
- Syafei WA. Implementation of K-best method for WLAN 802.11n MIMO decoder n. 2nd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE); 2015. p. 417–21. https://doi.org/10.1109/ICITACEE.2015.7437841
- Sangolli SV, Jayavignesh T.TCP throughput measurement and comparison of IEEE 802.11 legacy, IEEE 802.11n and IEEE 802.11ac standards. Indian Journal of Science and Technology. 2015; 8(20):1–8. https://doi.org/10.17485/ijst/2015/v8i1/83978
- Soleymani M, Maham B, Ashtiani F. Analysis of the downlink saturation throughput of an asymmetricIEEE 802.11-based WLAN. IEEE International Conference on Communications (ICC); 2016. p. 1–6.
- Yeong SY, Al-Salihy W, Wan TC. Indoor WLAN monitoring and planning using empirical and theoretical models propagation. 2nd International Conference on Network Applications, Protocols and Services; 2010. p. 165–9. https://doi.org/10.1109/NETAPPS.2010.36
- Corte AD, Gutierrez O, Gomez JM. High-accuracy localization based on the dominant rays of ray-tracing over fingerprinting techniques. Proceedings of the IEEE International Symposium on Antennas and Propagation; 2012. p. 1–2. https://doi.org/10.1109/APS.2012.6349084 PMid:21360142
- Desimone R, Brito BM, Baston J. Model of indoor signal propagation using log-normal shadowing. 2015 Long Island Systems, Applications and Technology; 2015. p. 1–4. PMCid:PMC4852957
- Andrade CB, Hoefel RF. IEEE 802. 11 WLANs: A comparison on indoor coverage models. CCECE; 2010. p. 1–6. PMid:20339799
- Model for Optimizing the Location of the Access Point in 802.11ac Networks Supported in the Model Log-Normal Shadowing
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Authors
Affiliations
1 Universidad Pontificia Bolivariana, Km 3 vía Piedecuesta, CO
2 Escuela de Ciencias Básicas Tecnología e Ingeniería (ECBTI), Universidad Nacional Abierta y a Distancia, Carrera 27 Nro. 40-43, Bucaramanga, CO
1 Universidad Pontificia Bolivariana, Km 3 vía Piedecuesta, CO
2 Escuela de Ciencias Básicas Tecnología e Ingeniería (ECBTI), Universidad Nacional Abierta y a Distancia, Carrera 27 Nro. 40-43, Bucaramanga, CO
Source
Indian Journal of Science and Technology, Vol 11, No 33 (2018), Pagination: 1-10Abstract
Background/Objectives: Designing a Wireless Local Area Network (WLAN) assumes great importance in determining the optimal placement of Access Points (APs) and assigning channels in order to achieve maximum levels of coverage and performance. The aim of this paper is to develop an optimization model for the location of the AP in indoor environments, the 2.4GHz and 5GHz, supported on the propagation model Log-Normal Shadowing. Methods/Statistical Analysis: To estimate the optimal location of the AP model, nonlinear optimization was proposed based on the probability cutting frequency bands, the dimensions of the environment, the transmission power, sensitivity receptor and the coverage radius, which two routines in Matlab for systematization model, supported in the propagation model Log-Normal Shadowing path loss, which allows developed decompose the received power at an average power and attenuation term shadow. Topic Relevance: Although there have been several related resource optimization work WLANs are very few studies have considered engaging in their research strategies for optimizing the geographic location of the AP. Aspect by which developed in Matlab routines may be used in future research related to the design of WLANs. Results: Based on the results it was evident that it is possible to predict the optimum location of the AP for the 2.4GHz and 5GHz, depending on the transmission power, the detection threshold of the receiver, the probability estimated cut and characterization of the environment between the AP, either free space or obstacles, supported the use of a shadow attenuation model. In addition, routines allowed establishing the Cartesian coordinates in which the location of the AP function of the radius of coverage, frequency band and environmental conditions, with 95% confidence is suggested. Application/Improvements: The developed routines can be used as support tools in future research work, related to the design and analysis of wireless networks that use the 2.4GHz and 5GHz bands, in order to evaluate aspects of interference, coverage, performance, efficiency and QoS.References
- Juan Carlos VF, Martha FCH, Perez WHE. Optimization of the spectral efficiency in WLAN networks in the 2.4GHz band under the use of allocation models. Indian Journal of Science and Technology. 2018; 11(22):1–13. https://doi.org/10.17485/ijst/2018/v11i22/122475
- Carlos VFJ, Contreras HMF, Perez WHE. Use of assignment models as a strategy for channel optimization in the 5GHz band supported in 802.11ac. Indian Journal of Science and Technology. 2018; 11(22):1–17. https://doi.org/10.17485/ijst/2018/v11i22/121348
- Lee Y, Kim K, Choi Y. Optimization of AP placement and channel assignment in wireless LANs. Proceedings 27th Annual IEEE Conference on Local Computer Networks, LCN; 2002. p. 831-836.
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- Haidar M, Ghimire R, Al-Rizzo H, Akl R, Yupo Chan. Channel assignment in an IEEE 802.11 WLAN based on Signal-to-Interference Ratio. Canadian Conference on Electrical and Computer Engineering; 2008. p. 1169–74. https://doi.org/10.1109/CCECE.2008.4564722
- Sangolli SV, Jayavignesh T. TCP throughput measurement and comparison of IEEE 802.11 legacy, IEEE 802.11n and IEEE 802.11ac Standards. Indian Journal of Science and Technology. 2015; 8(20):1–8. https://doi.org/10.17485/ijst/2015/v8i1/83978
- Andrade CB, Hoefel RF, Cheung KW. IEEE 802.11 WLANS: A Comparison on Indoor Coverage Models. Calgary. 2010; 1–6.
- Basha PH, Varma PS, Rajesh KS. QoS performance of IEEE 802.11 in MAC and PHY layer using Enhanced OAR Algorithm. Indian Journal of Science and Technology. 2017; 10(9):1–9. https://doi.org/10.17485/ijst/2017/v10i9/98054
- Zvanovec S, Pechac P, Klepal M. Wireless LAN networks design: Site survey or propagation modeling? Radio Engineering. 2003; 12(4):1–8.
- Yeong S-Y, Al-Salihy W, Wan T-C. Indoor WLAN monitoring and planning using empirical and theoretical propagation models. IEEE 2nd International Conference on Network Applications, Protocols and Services. 2010. p. 165–9. https://doi.org/10.1109/NETAPPS.2010.36
- Vesga FJC, Contreras HMF, Vesga BJA. Design of empirical propagation models supported in the Log-Normal Shadowing model for the 2.4GHz and 5GHz bands under Indoor environments. Indian Journal of Science and Technology. 2018; 11(22):1–18. https://doi.org/10.17485/ijst/2018/v11i20/122149
- Perez ES. Distancia Entre Dos Puntos; 2005. p. 1–7.
- Ravindranath NS, Singh I, Prasad A, Rao VS. Performance evaluation of IEEE 802.11ac and 802.11n using NS3. Indian Journal of Science and Technology. 2016; 9(26):1–9. https://doi.org/10.17485/ijst/2016/v9i26/93565
- Swokowski EW. Calculus with analytic geometry. Calculo con Geometría Analítica. 2nd ed. Mexico: Grupo Editorial Iberoamerica; 1989. p. 1–1097.
- Sivakumar P, Vinod B, Devi RSS, Rajkumar ERJ. Realtime task scheduling for distributed embedded system using MATLAB toolboxes. Indian Journal of Science and Technology. 2015; 8(15):1–7. https://doi.org/10.17485/ijst/2015/v8i15/55680
- Development of a Smart Meter, Power Line Communications Supported under IoT Architecture
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Authors
Affiliations
1 Escuela de Ciencias Basicas Tecnologia e Ingenieria (ECBTI), Universidad Nacional Abierta y a Distancia; Carrera 27 Nro. 40-43. Bucaramanga, CO
1 Escuela de Ciencias Basicas Tecnologia e Ingenieria (ECBTI), Universidad Nacional Abierta y a Distancia; Carrera 27 Nro. 40-43. Bucaramanga, CO
Source
Indian Journal of Science and Technology, Vol 11, No 42 (2018), Pagination: 1-15Abstract
Background/Objectives: Optimization of energy consumption has become the subject of ongoing research, in order to establish mechanisms that provide gradually to reduce global warming. In this paper the design, construction and implementation of a system of control and monitoring of power consumption (Smart Meter), supported on power line communications (PLC / Power Line Communications) under IoT architecture is proposed; which allows real-time measurements of various existing parameters in the power system at very low cost, using existing technology in the market. Methodology: To develop the prototype will use the grid as a physical transmission medium via a communication system HomePlug AV, a web server embedded under IP protocol for administering the communications system and control system power control comprised of a phase detector and zero crossing, an instrumentation system and a control system power supported state devices solid. Results: The developed prototype not only optimizes energy consumption of each electrical device, but also to measure in real time variables such as active power, reactive power and power factor; all operating on Internet Protocol (IP) and under using existing technology in the market. Relevance of the topic: The optimization of energy consumption is an issue of vital importance in the world and the development of this prototype raises the possibility of being implemented through the use of embedded systems of low cost and ease of implementation in homes in the future near. Application/Improvements: The developed prototype can be considered as a technological innovation strategy in the field of IoT, because it will allow to know in a very fast and efficient way the consumption of electrical energy product of a load, in terms of active power, reactive power, Parente power, power factor, RMS voltage and RMS current, variables of great importance in the residential or industrial context, which are quantified in an effective way depending on the waveforms and not through indirect methods such as performs conventionally on similar meters. In future related work it is recommended to establish mechanisms that allow to reduce the size of the hardware element, but also to make use of other alternative telecommunications technologies, in order to allow the development of low cost prototypes and ease of implementation.References
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