WOAIP: Wireless Optimization Algorithm for Indoor Placement Based on Binary Particle Swarm Optimization (BPSO)

Main Article Content

Omar S. Naif
Imad J. Mohammed

Abstract

Optimizing the Access Point (AP) deployment has a great role in wireless applications due to the need for providing an efficient communication with low deployment costs. Quality of Service (QoS), is a major significant parameter and objective to be considered along with AP placement as well the overall deployment cost. This study proposes and investigates a multi-level optimization algorithm called Wireless Optimization Algorithm for Indoor Placement (WOAIP) based on Binary Particle Swarm Optimization (BPSO). WOAIP aims to obtain the optimum AP multi-floor placement with effective coverage that makes it more capable of supporting QoS and cost-effectiveness. Five pairs (coverage, AP deployment) of weights, signal thresholds and received signal strength (RSS) measurements simulated using Wireless InSite (WI) software were considered in the test case study by comparing the results collected from WI with the present wireless simulated physical AP deployment of the targeted building - Computer Science Department at University of Baghdad. The performance evaluation of WOAIP shows an increase in terms of AP placement and optimization distinguished in order to increase the wireless coverage ratio to 92.93% compared to 58.5% of present AP coverage (or 24.5% coverage enhancement on average).

Downloads

Download data is not yet available.

Article Details

How to Cite
1.
Naif OS, Mohammed IJ. WOAIP: Wireless Optimization Algorithm for Indoor Placement Based on Binary Particle Swarm Optimization (BPSO). Baghdad Sci.J [Internet]. [cited 2021Dec.4];19(3):0605. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5948
Section
article

References

Wang H, Zhang P, Li J, You X. Radio propagation and wireless coverage of LSAA-based 5G millimeter-wave mobile communication systems. China Commun. 2019;16(5):1–18.

Parikh J, Basu A. Millimeter waves: Technological component for next-generation mobile networks. EAI/Springer Innov Commun Comput. 2019;167–86.

Meraj M, Kumar S. Evolution of mobile wireless technology from 0G to 5G. IJCSIT. 2015;6(3):2545-51.

Kárník J, Streit J. Summary of available indoor location techniques. IFAC-PapersOnLine [Internet]. 2016;49(25):311–7. Available from: http://dx.doi.org/10.1016/j.ifacol.2016.12.055

Hassan N, Yau KLA, Wu C. Edge computing in 5G: A review. IEEE Access. 2019;7:127276–89.

Talvitie J. Algorithms and Methods for Received Signal Strength Based Wireless Localization. Ph.D. Dissemination, Tampere University of Technology, http://urn.fi/URN:ISBN:978-952-15-3679-3. 2016.

Nurminen H, Dashti M, Piché R. A survey on wireless transmitter localization using signal strength measurements. Wirel Commun Mob Comput. 2017;2017:1-12.

Vettikalladi H, Sethi WT, Abas AF Bin, Ko W, Alkanhal MA, Himdi M. Sub-THz Antenna for High-Speed Wireless Communication Systems. Int J Antennas Propag. 2019;2019:1-9.

Jahagirdar S, Ghatak A, Kumar AA. WiFi based Indoor Positioning System using Machine Learning and Multi-Node Triangulation Algorithms. 2020 11th Int Conf Comput Commun Netw Technol ICCCNT 2020. 2020;1–6.

Theses M, Jin C. Trace: Tennessee Research and Creative Exchange A Site-Specific Indoor Wireless Propagation Model. 2004; Available from: https://trace.tennessee.edu/utk_gradthes/2559

Alshammari A. SN. Optimization of AP placement for Wireless LAN’s using genetic algorithm. Comput Eng Dep Kuwait Univ. 2006;2006:1-11.

Davis N. Comparison of Ray Tracing and Measurement Results for 5GHz Band Wireless Channels. 2009;53(9):1689–99.

Liu N, Plets D, Joseph W, Martens L. An algorithm for optimal network planning and frequency channel assignment in indoor WLANs. IEEE Antennas Propag Soc AP-S Int Symp. 2014;1177–8.

Oteri, Omae, Ndung’u E N KL. Wi-Fi Signal Indoor LOS Coverage modeling using PSO-ANFIS. In: Proceedings of Sustainable Research and Innovation Conference. 2018.

Abdulwahid MM, Al-Ani OA, Mosleh MF, Abd-Alhmeed RA. Optimal access point location algorithm based real measurement for indoor communication. InProceedings of the International Conference on Information and Communication Technology 2019 Apr 15 (pp. 49-55).

Mohammed RA, Salim ONM, Al-Nakkash AH, Alabdullah AAS. Proposed APs Distribution Optimization Algorithm: Aware of Interference (APD-AI). IOP Conf Ser Mater Sci Eng. 2020;745(1).

Wireless InSite, Reference Manual, Version 2.6.3, REMCOM Inc., 315 S. Allen St., Suite 416 State College, PA 16801, November 2012., Jan. 2009.

Sofoklis A. Kyriazakos GTK. Practical radio resource management in wireless systems. Artech House. 2004. ISBN: 9781580536332

Wang M, Liu Y, Li S, Zhang X. Simulation of 38 GHz millimeter-wave propagation characteristics in indoor LOS and NLOS environment. 2017 IEEE 6th Asia-Pacific Conf Antennas Propagation, APCAP 2017 - Proceeding. 2018;1–3.

Rawaa A M, Al-Nakkash AH, Salim ONM. A comparative study of indoor propagation models for IEEE 802.11n network. ACM Int Conf Proceeding Ser. 2019;69–73.

Liao Q. Ray-tracing based analysis of channel characteristics and capacity improvement capabilities of spatial multiplexing and beamforming at 15 and 28 GHz. 2016;1–99.

Series P. Effects of building materials and structures on radiowave propagation above about 100 MHz. Recommendation ITU-R. 2015:2040-1.

Yasear SA. Taxonomy of Memory Usage in Swarm Intelligence-Based Metaheuristics Abstract : 2019;16(march):445–52.

Bhuwania A, Subba P, Roy UK. Positioning wifi access points using particle swarm optimization. ICRCICN 2016 Sep 23, (pp. 112-115). IEEE.

Hasanova N. A Comparative Study of Particle Swarm Optimization and Genetic Algorithm. Qubahan Acad J. 2020;1(1):33–45.

Shahid Shabir DRS. A Comparative Study of Particle Swarm Optimization and Genetic Algorithm. Int J Electr Eng. 2016;1(1):215–23.