Research on Encryption Algorithm Conforming to AES in WLAN

2012 ◽  
Vol 532-533 ◽  
pp. 1517-1521
Author(s):  
Jia Li ◽  
Li Gan ◽  
Fang Fang Du
Keyword(s):  
Wireless Local Area Network ◽  
Computer Network ◽  
Local Area Network ◽  
Transmission Rate ◽  
Local Area ◽  
Encryption Algorithm ◽  
Area Network ◽  
Security Problem ◽  
Aes Encryption Algorithm ◽  
Simple Network

As the daughter of modern wireless communication technology & computer network technology, Wireless Local Area Network(WLAN) is being used widely in many areas. It is well known that WLAN has the advantages such as simple network configuration, high transmission rate, good extensibility and mobility, and convenience to carry, etc. However, its security mechanism is not perfect to guarantee its security because of some design flaws in safety precautions. The application of AES in WLAN improves its data confidentiality. The work presented in this paper focuses on present AES encryption algorithm security problem and optimization of its S Box. The improved algorithm this paper put forward in WLAN, is proved to be efficient.

Link-Level Simulator for Wireless local Area Network

2018 ◽  
Vol 6 (7) ◽  
pp. 314
Author(s):  
Chaithra. H. U ◽  
Vani H.R
Keyword(s):  
Wireless Local Area Network ◽  
Local Area Network ◽  
Local Area Networks ◽  
Local Area ◽  
Long Term Evolution ◽  
Wireless Local Area Networks ◽  
Area Network ◽  
Network Simulator ◽  
Term Evolution

Now a days in Wireless Local Area Networks (WLANs) used in different fields because its well-suited simulator and higher flexibility. The concept of WLAN  with  advanced 5th Generation technologies, related to a Internet-of-Thing (IOT). In this project, representing the Network Simulator (NS-2) used linked-level simulators for Wireless Local Area Networks and still utilized IEEE 802.11g/n/ac with advanced IEEE 802.11ah/af technology. Realization of the whole Wireless Local Area Networking linked-level simulators inspired by the recognized Vienna Long Term Evolution- simulators. As a outcome, this is achieved to link together that simulator to detailed performances of Wireless Local Area Networking with Long Term Evolution, operated in the similar RF bands. From the advanced 5th Generation support cellular networking, such explore is main because different coexistences scenario can arise linking wireless communicating system to the ISM and UHF bands.

PERANCANGAN ANTENA MIKROSTRIPRECTANGULAR

Jurnal Teknik ◽  
2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Heru Abrianto
Keyword(s):  
Dielectric Constant ◽  
Patch Antenna ◽  
Wireless Lan ◽  
Microstrip Antenna ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Local Area ◽  
Area Network ◽  
Dual Polarization ◽  
Feeding Technique

Microstrip antenna which designed with dual feeding at 2.4 GHz and 5.8 GHz can meet WLAN (Wireless Local Area Network) application.Antenna fabrication use PCB FR4 double layer with thickness 1.6 mm and dielectric constant value 4.4. The length of patch antenna according to calculation 28.63 mm, but to get needed parameter length of patch should be optimized to 53 mm. After examination, this antenna has VSWR 1.212 at 2.42 GHz and 1.502 at 5.8 GHz, RL -13.94 dB at 2.42 GHz and -20.357 dB at 5.8 GHz, gain of antenna 6.16 dB at 2.42 GHz and 6.91 dB at 5.8 GHz, the radiation pattern is bidirectional. Keywords : microstrip antenna, wireless LAN, dual polarization, single feeding technique

IEEE 802.11 Tutorial

2018 ◽  
Author(s):  
Kiramat
Keyword(s):  
Access Control ◽  
Ieee 802.11 ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Local Area ◽  
Physical Layer ◽  
Media Access Control ◽  
Area Network ◽  
Media Access ◽  
Computer Communications

IEEE 802.11 is a set of media access control (MAC) and physical layer (PHY) specifications for implementing wireless local area network (WLAN) computer communications. Maintained by the Institute of Electrical and Electronics Engineers (IEEE) LAN/MAN Standards Committee (IEEE 802). This document highlights the main features of IEEE 802.11n variant such as MIMO, frame aggregation and beamforming along with the problems in this variant and their solutions

scholarly journals Multi-terminal Experimental System Based on Wireless Local Area Network

2020 ◽  
Vol 1550 ◽  
pp. 032078
Author(s):  
Kaigang Fan ◽  
Xin Chen ◽  
Biao Zhao ◽  
Jiale Wang ◽  
Wenbin Cui ◽  
...  
Keyword(s):  
Wireless Local Area Network ◽  
Local Area Network ◽  
Experimental System ◽  
Local Area ◽  
Area Network

scholarly journals DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2000
Author(s):  
Marius Laska ◽  
Jörg Blankenbach
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Success Probability ◽  
Local Area ◽  
Position Estimation ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Area Network ◽  
Floor Plan

Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

Sign in / Sign up

Export Citation Format

Share Document