Detecting MAC Misbehavior of IEEE 802.11 Devices within Ultra Dense Wi-Fi Networks

The widespread deployment of IEEE 802.11 has made it an attractive target for potential attackers. The latest IEEE 802.11 standard has introduced encryption and authentication protocols that primarily address the issues of confidentiality and access control. However, improving network availability in the presence of misbehaving stations has not been addressed in the standard. Existing research addresses the problem of detecting misbehavior in scenarios without overlapping cells. However, in real scenarios cells overlap, resulting in a challenging environment for detecting misbehavior. The contribution of this paper is the presentation and evaluation of a new method for detecting misbehavior in this environment. This method is based on an objective function that uses a broad range of symptoms. Simulationresultsindicatethatthisnewapproachisverysensitive to misbehaving stations in ultra dense networks

Detecting MAC Misbehavior of IEEE 802.11 Devices within Ultra Dense Wi-Fi Networks

The widespread deployment of IEEE 802.11 has made it an attractive target for potential attackers. The latest IEEE 802.11 standard has introduced encryption and authentication protocols that primarily address the issues of confidentiality and access control. However, improving network availability in the presence of misbehaving stations has not been addressed in the standard. Existing research addresses the problem of detecting misbehavior in scenarios without overlapping cells. However, in real scenarios cells overlap, resulting in a challenging environment for detecting misbehavior. The contribution of this paper is the presentation and evaluation of a new method for detecting misbehavior in this environment. This method is based on an objective function that uses a broad range of symptoms. Simulationresultsindicatethatthisnewapproachisverysensitive to misbehaving stations in ultra dense networks

Introducing reinforcement learning in the Wi-Fi MAC layer to support sustainable communications in e-Health scenarios

The crisis of energy supplies has led to the need for sustainability in technology, especially in the Internet of Things (IoT) paradigm. One solution is the integration of Energy Harvesting (EH) technologies into IoT systems, which reduces the amount of battery replacement. However, integrating EH technologies within IoT systems is challenging, and it requires adaptations at different layers of the IoT protocol stack, especially at Medium Access Control (MAC) layer due to its energy-hungry features. Since Wi-Fi is a widely used wireless technology in IoT systems, in this paper, we perform an extensive set of simulations in a dense solar-based energy-harvesting Wi-Fi network in an e-Health environment. We introduce optimization algorithms, which benefit from the Reinforcement Learning (RL) methods to efficiently adjust to the complexity and dynamic behaviour of the network. We assume the concept of Access Point (AP) coordination to demonstrate the feasibility of the upcoming Wi-Fi amendment IEEE 802.11bn (Wi-Fi 8). This paper shows that the proposed algorithms reduce the network&amp;#x2019;s energy consumption by up to 25% compared to legacy Wi-Fi while maintaining the required Quality of Service (QoS) for e-Health applications. Moreover, by considering the specific adjustment of MAC layer parameters, up to 37% of the energy of the network can be conserved, which illustrates the viability of reducing the dimensions of solar cells, while concurrently augmenting the flexibility of this EH technique for deployment within the IoT devices. We anticipate this research will shed light on new possibilities for IoT energy harvesting integration, particularly in contexts with restricted QoS environments such as e-Healthcare.</p

Throughput and range characterization of IEEE 802.11ah

The most essential part of Internet of Things (IoT) infrastructure is the wireless communication system that acts as a bridge for the delivery of data and control messages. However, the existing wireless technologies lack the ability to support a huge amount of data exchange from many battery driven devices spread over a wide area. In order to support the IoT paradigm, the IEEE 802.11 standard committee is in process of introducing a new standard, called IEEE 802.11ah. This is one of the most promising and appealing standards, which aims to bridge the gap between traditional mobile networks and the demands of the IoT. In this paper, we first discuss the main PHY and MAC layer amendments proposed for IEEE 802.11ah. Furthermore, we investigate the operability of IEEE 802.11ah as a backhaul link to connect devices over a long range. Additionally, we compare the aforementioned standard with previous notable IEEE 802.11 amendments (i.e. IEEE 802.11n and IEEE 802.11ac) in terms of throughput (with and without frame aggregation) by utilizing the most robust modulation schemes. The results show an improved performance of IEEE 802.11ah (in terms of power received at long range while experiencing different packet error rates) as compared to previous IEEE 802.11 standards.Comment: 7 pages, 6 figures, 5 table

IEEE 802.11ax: challenges and requirements for future high efficiency wifi

The popularity of IEEE 802.11 based wireless local area networks (WLANs) has increased significantly in recent years because of their ability to provide increased mobility, flexibility, and ease of use, with reduced cost of installation and maintenance. This has resulted in massive WLAN deployment in geographically limited environments that encompass multiple overlapping basic service sets (OBSSs). In this article, we introduce IEEE 802.11ax, a new standard being developed by the IEEE 802.11 Working Group, which will enable efficient usage of spectrum along with an enhanced user experience. We expose advanced technological enhancements proposed to improve the efficiency within high density WLAN networks and explore the key challenges to the upcoming amendment.Peer ReviewedPostprint (author's final draft

A novel cheater and jammer detection scheme for IEEE 802.11-based wireless LANs

The proliferation of IEEE 802.11 networks has made them an easy and attractive target for malicious devices/adversaries which intend to misuse the available network. In this paper, we introduce a novel malicious entity detection method for IEEE 802.11 networks. We propose a new metric, the Beacon Access Time (BAT), which is employed in the detection process and inherits its characteristics from the fact that beacon frames are always given preference in IEEE 802.11 networks. An analytical model to define the aforementioned metric is presented and evaluated with experiments and simulations. Furthermore, we evaluate the adversary detection capabilities of our scheme by means of simulations and experiments over a real testbed. The simulation and experimental results indicate consistency and both are found to follow the trends indicated in the analytical model. Measurement results indicate that our scheme is able to correctly detect a malicious entity at a distance of, at least, 120 m. Analytical, simulation and experimental results signify the validity of our scheme and highlight the fact that our scheme is both efficient and successful in detecting an adversary (either a jammer or a cheating device). As a proof of concept, we developed an application that when deployed at the IEEE 802.11 Access Point, is able to effectively detect an adversary. (C) 2015 Elsevier B.V. All rights reserved.Postprint (author's final draft

Uplink Performance Optimization of Ultra Dense Wi-Fi Networks using AP-managed TPC

In this paper, we study the uplink transmission power control problem in ultra dense Wi-Fi networks and propose two novel access point-controlled frameworks, which determines optimum transmit power settings with the intention of maximizing an objective function. NS-3 simulation results show that the proposed centralized approaches reduce starvation among stations and significantly improves the objective function, resulting in improved performance

Detecting MAC Misbehavior of IEEE 802.11 Devices within Ultra Dense Wi-Fi Networks

The widespread deployment of IEEE 802.11 has made it an attractive target for potential attackers. The latest IEEE 802.11 standard has introduced encryption and authentication protocols that primarily address the issues of confidentiality and access control. However, improving network availability in the presence of misbehaving stations has not been addressed in the standard. Existing research addresses the problem of detecting misbehavior in scenarios without overlapping cells. However, in real scenarios cells overlap, resulting in a challenging environment for detecting misbehavior. The contribution of this paper is the presentation and evaluation of a new method for detecting misbehavior in this environment. This method is based on an objective function that uses a broad range of symptoms. Simulationresultsindicatethatthisnewapproachisverysensitive to misbehaving stations in ultra dense networks

Detecting MAC Misbehavior of IEEE 802.11 Devices within Ultra Dense Wi-Fi Networks

The widespread deployment of IEEE 802.11 has made it an attractive target for potential attackers. The latest IEEE 802.11 standard has introduced encryption and authentication protocols that primarily address the issues of confidentiality and access control. However, improving network availability in the presence of misbehaving stations has not been addressed in the standard. Existing research addresses the problem of detecting misbehavior in scenarios without overlapping cells. However, in real scenarios cells overlap, resulting in a challenging environment for detecting misbehavior. The contribution of this paper is the presentation and evaluation of a new method for detecting misbehavior in this environment. This method is based on an objective function that uses a broad range of symptoms. Simulationresultsindicatethatthisnewapproachisverysensitive to misbehaving stations in ultra dense networks