Application of bat algorithm for the detection of hidden nodes in IEEE802.11ah networks

The occurrence of the hidden node problem in IEEE802.11ah has increases by 41% as compared to previous versions of IEEE802.11 standards. This makes IEEE802.11ah network to be prone to experience high collision and low throughput. Previous efforts to solve this problem has mainly not addressed the issue of locating potential hidden nodes in the network. As a result, the hidden node problem in IEEE802.11ah still remains an open issue. This paper proposes an algorithm that applies bat algorithm for detecting hidden nodes in IEEE802.11ah networks. Our results have shown the effectiveness of this algorithm in detecting hidden nodes. This algorithm can be used to properly manage communication in IEEE802.11ah

IEEE 802.11ah Restricted Access Window Surrogate Model for Real-Time Station Grouping

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Enabling wireless closed loop communication : optimal scheduling over IEEE 802.11ah networks

Industry 4.0 is being enabled by a number of new wireless technologies that emerged in the last decade, aiming to ultimately alleviate the need for wires in industrial use cases. However, wireless solutions are still neither as reliable nor as fast as their wired counterparts. Closed loop communication, a representative industrial communication scenario, requires high reliability (over 99%) and hard real-time operation, having very little tolerance for delays. Additionally, connectivity must be provided over an entire industrial side extending across hundreds of meters. IEEE 802.11ah fits this puzzle in terms of data rates and range, but it does not guarantee deterministic communication by default. Its Restricted Access Window (RAW), a new configurable medium access feature, enables flexible scheduling in dense, large-scale networks. However, the standard does not define how to configure RAW. The existing RAW configuration strategies assume uplink traffic only and are dedicated exclusively to sensors nodes. In this article, we present an integer nonlinear programming problem formulation for optimizing RAW configuration in terms of latency in closed loop communication between sensors and actuators, taking into account both uplink and downlink traffic. The model results in less than 1% of missed deadlines without any prior knowledge of the network parameters in heterogeneous time-changing networks

An analytical model for the aggregate throughput of IEEE 802.11ah networks under the restricted access window mechanism

The IEEE 802.11ah is an amendment to the IEEE 802.11 standard to support the growth of the Internet of Things (IoT). One of its main novelties is the restricted access window (RAW), which is a channel access feature designed to reduce channel contention by dividing stations into RAW groups. Each RAW group is further divided into RAW slots, and stations only attempt channel access during the RAW slot they were assigned to. In this paper, we propose a discrete-time Markov chain model to evaluate the average aggregate throughput of IEEE 802.11ah networks using the RAW mechanism under saturated traffic and ideal channel conditions. The proposed analytical model describes the behavior of an active station within its assigned RAW slot. A key aspect of the model is the consideration of the event of RAW slot time completion during a station’s backoff operation. We study the average aggregate network throughput for various numbers of RAW slots and stations in the network. The numerical results derived from our analytical model are compared to computer simulations based on an IEEE 802.11ah model developed for the ns-3 simulator by other researchers, and its performance is also compared to two other analytical models proposed in the literature. The presented results indicate that the proposed analytical model reaches the closest agreement with independently-derived computer simulations

Modelagem analítica da vazão de redes sem fio baseadas na norma IEEE 802.11ah

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2018.Inúmeras tecnologias promissoras foram desenvolvidas desde a concepção da Internet das Coisas (IoT, do inglês Internet of Things) que poderão modificar diferentes aspectos em nosso cotidiano. Tendo em vista o rápido crescimento da IoT, foi criado o grupo de tarefa IEEE 802.11ah para o desenvolvimento de uma nova norma Wi-Fi para lidar com os principais desafios da IoT, que são a conectividade de muitos dispositivos com recursos de energia limitados a um único ponto de acesso e o aumento do alcance das transmissões para distâncias muito maiores do que aquelas tipicamente utilizadas em redes locais sem fio. Assim, esse padrão traz novos mecanismos nas camadas física e de enlace para atender aos requisitos da IoT. Na camada física, houve modificações para operação em faixas de frequências abaixo de 1 GHz e, na camada MAC, o padrão traz novos mecanismos de economia de energia para ter um melhor desempenho em redes densas, sendo um deles o acesso restrito ao canal (RAW, do inglês Restricted Access Window). Recentemente, há muitos trabalhos sobre o IEEE 802.11ah, entretanto, a maior parte deles trazem ideias de agrupamento ou modelagem analítica para descobrirem o tamanho ótimo do RAW para obter maior desempenho da rede. No entanto, para um melhor entendimento dos efeitos dos diversos parâmetros e mecanismos do IEEE 802.11ah, esta dissertação apresenta um modelo analítico da função de coordenação distribuída do IEEE 802.11ah para o caso em que as estações estão distribuídas uniformemente em diversos slots RAW e possuem a mesma prioridade de tráfego, sob condições de tráfego saturado e canal ideal. Em nosso modelo, consideramos que cada intervalo de beacon possui somente um RAW o qual é dividido em slots RAW, e estudamos o impacto no desempenho da vazão ao variar o número de slots RAW e o número de estações na rede. Além disto, introduzimos ao modelo Markoviano uma probabilidade de término do slot RAW e propomos duas expressões empíricas para o cálculo desta probabilidade. Por fim validamos o modelo analítico com simulações no simulador de redes NS3 e, a partir das simulações computacionais, encontramos uma constante de ajuste para aproximar o modelo das simulações.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Many promising technologies have been developed since the conception of the Internet of Things (IoT) that can modify different aspects of our daily lives. In light of the rapid growth of IoT, it was created the IEEE 802.11ah Task Group to develop a newWi-Fi standard to address IoT’s key challenges, which are the connectivity of many devices with limited power resources to a single access point and the increase in the transmissions range for distances much greater than those typically used in wireless local area networks. Thus, this standard brings new mechanisms in the physical and link layers to meet IoT requirements. In the physical layer, there were modifications to permit the operation in frequency bands below 1 GHz and, in the MAC layer, the standard brings new power saving mechanisms to perform better in dense networks, one of which is the restricted access to the channel (RAW). Recently, there have been a lot of work on IEEE 802.11ah, however, most of them bring grouping or analytical modeling ideas to find out the optimal RAW size for a higher network performance. On the other hand, for a better understanding of the effects of the various IEEE 802.11ah parameters and mechanisms, this dissertation presents an analytical model of the distributed coordination function of IEEE 802.11ah for the case where the stations are evenly distributed in several RAW slots and have the same traffic priority, under conditions of saturated traffic and ideal channel. In our model, we consider that each beacon interval has only one RAW which is divided into RAW slots, and we study the impact on throughput performance by varying the number of RAW slots and the number of stations in the network. In addition, we introduce to the Markovian model a probability of reaching the end of a RAW slot and propose two heuristic expressions for this probability calculation. Finally we validate the analytical model with simulations in the networks simulator NS3 and, from the computational simulations, we find an adjustment constant to approximate the model of the simulations

A Grouping Algorithm to Alleviate the Hidden Node Problem in 802.11ah Networks

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 박세웅.802.11ah에서는 전송거리가 1km 이상 되고 최대 8000여개의 노드를 서비스할 수 있다. 따라서 802.11ah 네트워크에서는 기존의 802.11 보다 더 많은 히든 노드 쌍을 가지게 된다. 특히나 저전력 모드에서 단말이 AP에게 PS-Poll을 전송할 때 히든 쌍에 의한 문제는 더욱 심해지고 히든 노드 쌍으로 인한 패킷 충돌이 빈번히 일어나기 때문에 전반적인 네트워크 성능이 저하된다. 따라서 본 논문은 802.11ah 저전력 모드에서 발생할 수 있는 히든 노드 문제를 조명하고 그 문제를 완화시키기 위한 알고리즘을 제안한다. 제안하는 알고리즘에서는 먼저 히든 노드를 찾아내고 그 정보를 바탕으로 히든 노드 행렬을 만든다. 그리고 히든 노드 행렬을 기반으로 그룹을 재편성 시켜주는 순서로 진행이 된다. 모의 실험을 통하여 제안하는 기법이 히든 쌍을 거의 소거하는 것을 확인하였다. 결과적으로 802.11ah 네트워크의 수율과 전송 지연이 크게 개선된 것을 확인하였다.IEEE 802.11ah offers a transmission range of up to 1km and about 8000 nodes are handled by a single access point (AP). As a result, 802.11ah networks have more hidden node pairs than 802.11a/b/g/n/ac networks. Especially, when a node sends a PS-Poll frame to an AP in the power saving mode, the hidden node problem is aggravated, resulting in frequent packet collisions. In this paper, we propose a grouping algorithm to alleviate the hidden node problem, which consists of three steps. At first, it finds hidden pairs in a network and, second generates a hidden node matrix accordingly. Then, the algorithm regroups the hidden nodes using the hidden node matrix. Through extensive simulations, we showed that our proposed algorithm almost eliminates the hidden node pairs. Therefore, our proposed algorithm improves network performances such as throughput and delay in 802.11ah networks.Contents Abstract i Contents ii List of Tables iv List of Figures v 1. Introduction 1 2. Overview of 802.11ah 4 2.1 PHY layer.................................................................................................. 6 2.1.1 Channelization............................................................................ 6 2.2 MAC layer................................................................................................ 7 2.2.1 Power Saving Mode (PSM)................................................. 7 2.2.2 Grouping Method..................................................................... 8 3. System Model . 9 4. Proposed Scheme: Hidden node Matrix Regrouping (HMR) 10 4.1 Hidden Node Detection....................................................................... 10 4.2 Hidden Node Matrix Generation .................................................... 12 4.3 Hidden Node Regrouping ................................................................. 13 5. Analysis 15 5.1 Finding the Number of Groups........................................................ 15 6. Simulation 17 6.1 The Number of Hidden Node Pairs............................................... 18 6.2 PS-Poll Transmission Completed Time........................................ 20 6.3 Retransmission Number...................................................................... 22 6.4 Regrouping Result................................................................................ 23 7. Conclusion 24 Reference ............. 25Maste