scholarly journals A QoS-aware Scheduling with Node Grouping for IEEE 802.11ah

2021 ◽  
Author(s):  
Nurzaman Ahmed ◽  
Iftekhar Hussain
Keyword(s):  
Large Scale ◽  
Network Reliability ◽  
Markov Chain Model ◽  
Traffic Load ◽  
Chain Model ◽  
Medium Access ◽  
Traffic Scheduling ◽  
Scheduling Scheme ◽  
Ieee 802.11Ah ◽  
Critical Traffic

Abstract The recent IEEE 802.11ah amendment has proven to be suitable for supporting large-scale devices in Internet of Things (IoT). It is essential to provide a minimum level of Quality of Service (QoS) for critical applications such as industrial automaton and healthcare. In this paper, we propose a QoSaware Medium Access Control (MAC) layer solution to enhance network reliability and reduce critical traffic latency by an adaptive station grouping and a priority traffic scheduling scheme. The proposed grouping scheme calculates the current traffic load and distributes among different RAW groups considering different requirements of the stations. The RAW scheduling scheme further provides priority slot access using a novel backoff scheme. Markov-chain model is developed to study the throughput and latency behaviours for the traffic generated from the critical application. The proposed protocol shows significant delay improvement for priority traffic. The overall throughput performance improves up to 12.7% over the existing RAW grouping scheme.

scholarly journals Performance Analysis of Synchronous Multi-Radio Multi-Link MAC Protocols in IEEE 802.11be Extremely High Throughput WLANs

2020 ◽  
Vol 11 (1) ◽  
pp. 317
Author(s):  
Taewon Song ◽  
Taeyoon Kim
Keyword(s):  
Ieee 802.11 ◽  
Markov Chain Model ◽  
Careful Consideration ◽  
Mac Protocols ◽  
Media Access Control ◽  
Chain Model ◽  
Distributed Coordination ◽  
Media Access ◽  
Medium Access ◽  
Coordination Function

The representative media access control (MAC) mechanism of IEEE 802.11 is a distributed coordination function (DCF), which operates based on carrier-sense multiple access with collision avoidance (CSMA/CA) with binary exponential backoff. The next amendment of IEEE 802.11 being developed for future Wi-Fi by the task group-be is called IEEE 802.11be, where the multi-link operation is mainly discussed when it comes to MAC layer operation. The multi-link operation discussed in IEEE 802.11be allows multi-link devices to establish multiple links and operate them simultaneously. Since the medium access on a link may affect the other links, and the conventional MAC mechanism has just taken account of a single link, the DCF should be used after careful consideration for multi-link operation. In this paper, we summarize the DCFs being reviewed to support the multi-radio multi-link operation in IEEE 802.11be and analyze their performance using the Markov chain model. Throughout the extensive performance evaluation, we summarize each MAC protocol’s pros and cons and discuss essential findings of the candidate MAC protocols.

scholarly journals IEEE 802.11ah: A Long Range 802.11 WLAN at Sub 1 GHz

2013 ◽  
pp. 83-108
Author(s):  
Weiping Sun ◽  
Munhwan Choi ◽  
Sunghyun Choi
Keyword(s):  
Wireless Lan ◽  
Large Scale ◽  
Transmission Range ◽  
Mac Layer ◽  
Medium Access ◽  
Frequency Spectra ◽  
Ieee 802.11Ah ◽  
Phy Layer ◽  
5 Ghz ◽  
Access Mechanisms

IEEE 802.11ah is an emerging Wireless LAN (WLAN) standard that defines a WLAN system operating at sub 1 GHz license-exempt bands. Thanks to the favorable propagation characteristics of the low frequency spectra, 802.11ah can provide much improved transmission range compared with the conventional 802.11 WLANs operating at 2.4 GHz and 5 GHz bands. 802.11ah can be used for various purposes including large scale sensor networks, extended range hotspot, and outdoor Wi-Fi for cellular traffic offloading, whereas the available bandwidth is relatively narrow. In this paper, we give a technical overview of 802.11ah Physical (PHY) layer and Medium Access Control (MAC) layer. For the 802.11ah PHY, which is designed based on the down-clocked operation of IEEE 802.11ac’s PHY layer, we describe its channelization and transmission modes. Besides, 802.11ah MAC layer has adopted some enhancements to fulfill the expected system requirements. These enhancements include the improvement of power saving features, support of large number of stations, efficient medium access mechanisms and throughput enhancements by greater compactness of various frame formats. Through the numerical analysis, we evaluate the transmission range for indoor and outdoor environments and the theoretical throughput with newly defined channel access mechanisms.

scholarly journals Modeling of Wireless Traffic Load in Next Generation Wireless Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Sohaib Manzoor ◽  
Khalid Bashir Bajwa ◽  
Muhammad Sajid ◽  
Hira Manzoor ◽  
Mahak Manzoor ◽  
...  
Keyword(s):  
Markov Chain Model ◽  
Search Tree ◽  
Base Stations ◽  
Traffic Load ◽  
Binary Search Tree ◽  
Chain Model ◽  
New Paradigm ◽  
Wifi Networks ◽  
Whale Optimization ◽  
Handover Mechanism

Software defined WiFi network (SD-WiFi) is a new paradigm that addresses issues such as mobility management, load management, route policies, link discovery, and access selection in traditional WiFi networks. Due to the rapid growth of wireless devices, uneven load distribution among the network resources still remains a challenging issue in SD-WiFi. In this paper, we design a novel four-tier software defined WiFi edge architecture (FT-SDWE) to manage load imbalance through an improved handover mechanism, enhanced authentication technique, and upgraded migration approach. In the first tier, the handover mechanism is improved by using a simple AND operator and by shifting the association control to WAPs. Unauthorized user load is mitigated in the second tier, with the help of base stations (BSs) which act as edge nodes (ENs), using elliptic ElGamal digital signature algorithm (EEDSA). In the third tier, the load is balanced in the data plane among the OpenFlow enabled switches by using the whale optimization algorithm (WOA). Moreover, the load in the fourth tier is balanced among the multiple controllers. The global controller (GC) predicts the load states of local controllers (LCs) from the Markov chain model (MCM) and allocates packets to LCs for processing through a binary search tree (BST). The performance evaluation of FT-SDWE is demonstrated using extensive OMNeT++ simulations. The proposed framework shows effectiveness in terms of bandwidth, jitter, response time, throughput, and migration time in comparison to SD-WiFi, EASM, GAME-SM, and load information strategy schemes.

scholarly journals Analytical Models and Performance Evaluation of Vehicular-to-Infrastructure Networks With Optimal Retransmission Number

2021 ◽  
Author(s):  
Aytül Bozkurt
Keyword(s):  
Vehicular Networks ◽  
Markov Chain Model ◽  
Arrival Rate ◽  
High Rate ◽  
Traffic Load ◽  
Analytical Models ◽  
Traffic Model ◽  
Chain Model ◽  
Queuing Model ◽  
The Impact

Abstract Vehicle-to-infrastructure and vehicle-to-vehicle communications has been introduced to provide high rate Internet connectivity to vehicles to meet the ubiquitous coverage and increasing high-data rate internet and multimedia demands by utilizing the 802.11 access points (APs). In order to evaluate the performance of vehicular networks over WLAN, in this paper, we investigate the transmisison and network performance of vehicles that pass through AP by considering contention nature of vehicles over 802.11 WLANs. Firstly, we derived an analytical traffic model to obtain the number of vehicles under transmision range of an AP. Then, incorporating vehicle traffic model with Markov chain model and for arrival packets, M/G/1/K queuing system, we developed a model evaluating the performance of DCF mechanism with an optimal retransmission number. We also derived the probability of mean arrival rate l to AP. A distinctive aspect of our proposed model is that it incorporates both vehicular traffic model and backoff procedure with M/G/1/K queuing model to investigate the impact of various traffic load conditions and system parameters on the vehicular network system. Based on our model, we show that the delay and througput performance of the system reduces with the increasing vehicle velocity due to optimal retransmision number m, which is adaptively adjusted in the network with vehicle mobility.

scholarly journals Shallow seismicity in the area of Greece: its character as seen by means of a stochastic model

1995 ◽  
Vol 2 (3/4) ◽  
pp. 136-146 ◽  
Author(s):  
L. C. Polimenakos
Keyword(s):  
Markov Chain ◽  
Large Scale ◽  
Markov Chain Model ◽  
Time Interval ◽  
Chain Model ◽  
Earthquake Occurrence ◽  
Chain Analysis ◽  
Order Markov Chain ◽  
Occurrence Pattern ◽  
Of Greece

Abstract. Occurrence of successive earthquake events in space is analysed by means of semi-stochastic processes. The analysis employs earthquakes events with M > 5.2 from the area of Greece and its surroundings (18-31° E, 34-43° N) for the time interval 1911-1985. The sequence of earthquake occurrences can be only marginally described by a first order Markov chain model. Substitutability analysis incorporates the results of Markov Chains, revealing, though, detailed interrelations of parts (subareas) of the study area, not appreciated in Markov Chain analysis. Reactivation of particular subareas provides an insight into the level of interaction between neighbouring seismogenic sources within a subarea. The earthquake occurrence pattern provides evidence for the effect of a significant stress diffusion through time in the sense of a stress front. Taking into account the limitations of the methodologies applied, results indicate the importance of large-scale monitoring of seismicity, which assist in the identification of particular characteristics of the earthquake occurrence in space and time.

scholarly journals Feedback-Based Self-Learning in Large-Scale Conversational AI Agents

2020 ◽  
Vol 34 (08) ◽  
pp. 13180-13187
Author(s):  
Pragaash Ponnusamy ◽  
Alireza Roshan Ghias ◽  
Chenlei Guo ◽  
Ruhi Sarikaya
Keyword(s):  
Large Scale ◽  
Markov Chain Model ◽  
Learning System ◽  
Chain Model ◽  
Manual Annotation ◽  
Defect Rate ◽  
Anonymized Data ◽  
The Right ◽  
Recurrent Patterns ◽  
Self Learning

Today, most of the large-scale conversational AI agents such as Alexa, Siri, or Google Assistant are built using manually annotated data to train the different components of the system including Automatic Speech Recognition (ASR), Natural Language Understanding (NLU) and Entity Resolution (ER). Typically, the accuracy of the machine learning models in these components are improved by manually transcribing and annotating data. As the scope of these systems increase to cover more scenarios and domains, manual annotation to improve the accuracy of these components becomes prohibitively costly and time consuming. In this paper, we propose a system that leverages customer/system interaction feedback signals to automate learning without any manual annotation. Users of these systems tend to modify a previous query in hopes of fixing an error in the previous turn to get the right results. These reformulations, which are often preceded by defective experiences caused by either errors in ASR, NLU, ER or the application. In some cases, users may not properly formulate their requests (e.g. providing partial title of a song), but gleaning across a wider pool of users and sessions reveals the underlying recurrent patterns. Our proposed self-learning system automatically detects the errors, generate reformulations and deploys fixes to the runtime system to correct different types of errors occurring in different components of the system. In particular, we propose leveraging an absorbing Markov Chain model as a collaborative filtering mechanism in a novel attempt to mine these patterns. We show that our approach is highly scalable, and able to learn reformulations that reduce Alexa-user errors by pooling anonymized data across millions of customers. The proposed self-learning system achieves a win-loss ratio of 11.8 and effectively reduces the defect rate by more than 30% on utterance level reformulations in our production A/B tests. To the best of our knowledge, this is the first self-learning large-scale conversational AI system in production.

Type II Sleep Mode Operation for IEEE 802.16e Based WiMAX

2011 ◽  
Vol 128-129 ◽  
pp. 343-349
Author(s):  
Jian Bin Xue ◽  
Song Bai Li ◽  
Ting Zhang ◽  
Wen Hua Wang
Keyword(s):  
Energy Efficiency ◽  
Energy Saving ◽  
Markov Chain Model ◽  
Broad Band ◽  
Traffic Load ◽  
Sleep Mode ◽  
Chain Model ◽  
Ieee 802.16E ◽  
Mode Operation ◽  
Set Up

To overcome the flaw of energy efficiency drop in broad band wireless communication with the short time sleep, the energy-saving mechanism of the sleep mode operation was researched in IEEE 802.16e. In this paper we propose a dynamic algorithm to tune the ratio of the sleep windows and receive windows according to the traffic load. Then, a Markov chain model was set up to analyze the energy efficiency and mean access delay. NS2 simulation results show that the proposed algorithm can achieve marked gain in energy efficiency compared to the traditional energy saving mechanism.

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