Teaching and Learning Wi-Fi Networking Fundamentals Using Limited Resources

Motivating students to learn Wi-Fi (wireless fidelity) wireless networking to undergraduate students is often difficult because many students find the subject rather technical and abstract when presented in traditional lecture format. This paper focuses on the teaching and learning aspects of Wi-Fi networking using limited hardware resources. It provides a walk-through tutorial on setting up Wi-Fi networks using wireless laptops and access points. Students can easily set up and configure Wi-Fi networks using relatively few computing resources to learn networking concepts more effectively. By measuring the key performance metrics such as network throughput and response times, students are able to gain a deeper understanding of Wi-Fi network performance and related issues. The effectiveness of these Wi-Fi practical learning activities has been evaluated both formally by students and informally in discussion within the teaching team. This paper describes the overall effectiveness of teaching and learning Wi-Fi network fundamentals using limited resources.

Wi-Fi networks are becoming more and more ubiquitous and represent a substantial source of energy consumption around the globe, mainly when it comes to Access Points (APs). There has been some work done on the characterization of the power consumption of Wi-Fi APs and network interface cards (NICs), and the power usage of these devices under different configurations and standards but mostly using legacy standards. A detailed AP power consumption analysis, exploring the whole set of degrees of freedom and capabilities of these devices is lacking in the state of the art. In this paper, we present a thorough power consumption analysis, covering the configuration options available in enterprise Wi-Fi APs from the three major vendors on the market. The goal is to understand how the power consumption of an AP varies with the different configurations, and provide insights on the parameters that significantly affect the AP power consumption. The obtained experimental results confirm previous state-of-the-art conclusions but contradict some of the studies and results found in the literature, while updating results and conclusions taken in the past to the most recent standards, configurations, and data rates available today. The analysis provided herein is a valuable source of information for deriving new AP power consumption models and designing energy-efficient Wi-Fi networks.

Wireless fidelity (WiFi) networks are deployed in several varied environments all around the World. Usually, the wireless fidelity access points are always on in houses and other small companies. In buildings of large companies and public organizations and in university campuses the number of access points is elevated; they are powered using power over the ethernet and are always on. Consequently, they consume a considerable amount of electric energy. The last versions of the International Electric and Electronic Engineers 802.11 standardized procedures to save energy in a wireless fidelity terminal but not in the access point. We designed a formal method to show when energy can be saved in wireless fidelity access points considering different power supplies for the access point: an electric energy battery and a standard voltage supply. We use an external battery that stores electric energy during an interval of time from a standard voltage supply (Charge period). After that interval (Discharge period), the energy supply for the access point is the external battery. Those intervals of time are repeated sequentially (Charge and Discharge cycles). We verified our formal model implementing a hardware circuit that controls the power supply for the access point. The amount of energy saving for a large number of of access points during a long period of time is considerably high.

Wi-Fi is the dominant infrastructure for last-mile internet access both in indoor and outdoor environments. The existing Wi-Fi infrastructure leverages to offer IoT services. Despite the growth and popularity of Wi-Fi, security is a significant concern associated with it. One such issue with the Wi-Fi network is Evil Twin Attack (ETA). In ETA, the adversary creates an Evil Twin AP (EAP), a copy of Legitimate Access Point (LAP), for the user to connect. Once the user connects to EAP, the adversary can perform various attacks. Enterprise networks can solve ETA with the help of certificate-based server authentication using 802.1X and Extensible Authentication Protocol. However, there is no proper solution to detect ETA in Wi-Fi personal networks. Latest Wi-Fi security algorithms, namely Wi-Fi Protected Access 3 (WPA3) and Opportunistic Wireless Encryption (OWE) also vulnerable to ETA. This paper proposes a novel Proof of Existence (PoEx) scheme, bringing network lifetime to Access Point (AP). Wi-Fi client devices will detect and prevent ETA using associated AP lifetime. We apply the proposed PoEx scheme on WPA3 and OWE and show the security improvements. Our experiment results show that lifetime forging is difficult due to an exponential number of computations (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(N-1)</sup> ) required. The experimental result also shows negligible overhead (less than 2%) on throughput when the PoEx solution is enabled.

기존의 보안 Wi-Fi 네트워크는 사용자가 AP(Access Point)의 암호에 맞춰야 하므로 사용이 불편하며, 사용자들이 암호를 공유하므로 시간이 갈수록 보안성이 낮아지는 문제점을 갖고 있다. 이를 해결하기 위해 사용자마다 별도의 가상 Wi-Fi 네트워크를 할당하는 방안을 제안한다. 이 방법에서는 각 사용자가 자신만의 Wi-Fi 네트워크를 가지므로 사용자 중심의 네트워크 설정이 가능하다. 사용자는 자신의 기기에 나름대로의 SSID(Service Set IDentifier)와 암호를 미리 설정해 두며 AP는 자신의 공개키를 적절한 방법으로 공개한다. AP는 또한 사용자들이 언제나 접속할 수 있는 공개채널을 유지한다. 사용자 요청이 있을 때 사용자 기기는 연결 요청 메시지를 보내는데 이 메시지에는 AP의 공개키로 암호화된 사용자 기기의 SSID와 암호가 실려 있다. 연결 요청 메시지를 받은 AP는 해당 SSID 및 암호가 설정된 새로운 가상의 AP를 생성하는데 이 가상 AP는 해당 사용자만 사용할 수 있는 전용 AP라고 할 수 있다. 이렇게 만들어지는 가상 네트워크는 비밀번호를 여러 사용자가 공유하지 않으므로 보안성이 높다. 또 이 가상 네트워크는 네트워크가 사용자 기기에 맞춰 스스로를 설정하기 때문에 사용의 편의성이 높다. 새 방법이 제공하는 보안성과 편리성에도 불구하고 기존의 Wi-Fi 네트워크에 비해 별다른 전송 능력 저하는 나타나지 않음을 실험을 통해 확인하였다. Existing Wi-Fi networks require users to follow network settings of the AP (Access Point), resulting in inconveniences for users, and the password of the AP is shared by all users connected to the AP, causing security information leaks as time goes by. We propose, in this work, a personalized secure Wi-Fi network, in which each user is assigned her own virtual Wi-Fi network. One virtual Wi-Fi per user makes the user-centric network configuration possible. A user sets a pair of her own SSID and password on her device a priori, and the AP publishes its public key in a suitable way. The AP also maintains an open Wi-Fi channel, to which users can connect anytime. On user's request, the user device sends a connection request message containing a pair of SSID and password encrypted with the AP's public key. Receiving the connection request message, the AP instantiates a new virtual AP secured with the pair of SSID and password, which is dedicated to that single user device. This virtual network is securer because the password is not shared among users. It is more convenient because the network adapts itself to the user device. Experiments show that these advantages are obtained with negligible degradation in the throughput performance.

Performance analysis for MU-MIMO enabled LTE-U networks coexisting with D2D and WiFi network

Wi-Fi networks are largely served using over-the-counter commodity Wi-Fi routers, access points (APs), and possibly with wireless controllers. Existing approaches to bring the benefits of Software-defined Networking (SDN) to such Wi-Fi networks suffer from availability, hardware requirements, and scalability issues. For instance, these approaches do not support enterprise APs whose firmware cannot be modified. Furthermore, we observe that using SDN controllers for managing all the flows traversing these APs and routers requires more than just installing an SDN switch on these devices. In this paper, we present our solution called SWIFT: Software-defined Wi-Fi Flow Management, for bringing SDN-based flow management to Wi-Fi networks using commodity Wi-Fi APs, Wi-Fi routers, and Wi-Fi controllers. We also detail its benefits, shortcomings, and possible use cases. Specifically, our solution significantly lowers the barrier-to-entry for deploying and conducting research on software-defined Wi-Fi networks.

Aiming at the urgent demands on constructing WiFi communication networks under emergency environments such as rescue and relief work, FC-WiFi, a WiFi network based on OpenFlow was proposed. FC-WiFi employs name-based routing algorithm, and has the characteristics as follows: (i) supporting free configuration access for mobile hosts, (ii) sharing information among mobile hosts and servers, (iii) maintaining the ongoing connection during mobile hosts handover between access points. A prototype system is implemented and experiments show that, hosts can communicate with each other when they associate to FC-WiFi with free configuration. The mobile host can keep TCP connections uninterrupted while passing through multiple access points and the handover delay is under the influence of the original configuration.

&lt;p&gt;WiFi networks based on the IEEE 802.11 standard are widely used indoors or outdoors as simple and cost-effective wireless technology. However, the data connection is significantly disrupted when mobile stations (STAs) switch between access points (APs). Furthermore, high packet loss occurs during the switching period. Therefore, mobility is a critical issue that needs to be solved in WiFi networks. In cellular networks, handover is used to keep ongoing data transfer when network clients switch between base stations. However, the handover algorithm is not supported in the 802.11 standard for WiFi networks. Self-Organizing Network (SON) functionality enables seamless handover in cellular networks, improving network performance. However, the SON functionality has not been fully researched in WiFi networks, especially for mobility management. Motivated by the SON functionalities, a SON approach is proposed to automatically optimize the handover algorithms for WiFi networks. This approach focuses on the SON functionalities including self-configuration, self-optimization and self-healing using machine learning techniques to develop new algorithms for WiFi mobility management. The overall goal of this thesis is to optimize handover performance as well as enhance the network’s capabilities.&lt;/p&gt;

Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets) have been recently proposed as a promising technique for indoor wireless communications. Such a network combines the high-speed transmission of LiFi and the ubiquitous coverage of WiFi. Meanwhile, in addition to user mobility, intermittent light-path blockages make the handover issue intricate in HLWNets. Also, load balancing (LB) is necessary because the coverage areas of LiFi and WiFi networks are completely overlapped. This further increases the difficulty in handover as the decision for a vertical or a horizontal handover in a mobile environment with ultra-small cells is non-trival. Aimed at providing an optimal solution to the access point selection (APS) with given channel state information (CSI), the conventional LB method might cause frequent and unnecessary handovers. In this paper, a joint optimisation problem is formulated to simultaneously consider LB and handover in HLWNets. Results show that compared with the conventional LB method, the proposed method can improve the system throughput by up to 60%.

This paper explores the feasibility of leveraging concepts from deep reinforcement learning (DRL) to enable dynamic resource management in Wi-Fi networks implementing distributed multi-user MIMO (D-MIMO). D-MIMO is a technique by which a set of wireless access points are synchronized and grouped together to jointly serve multiple users simultaneously. This paper addresses two dynamic resource management problems pertaining to D-MIMO Wi-Fi networks: (i) channel assignment of D-MIMO groups, and (ii) deciding how to cluster access points to form D-MIMO groups, in order to maximize user throughput performance. These problems are known to be NP-Hard and only heuristic solutions exist in literature. We construct a DRL framework through which a learning agent interacts with a D-MIMO Wi-Fi network, learns about the network environment, and is successful in converging to policies which address the aforementioned problems. Through extensive simulations and on-line training based on D-MIMO Wi-Fi networks, this paper demonstrates the efficacy of DRL in achieving an improvement of 20% in user throughput performance compared to heuristic solutions, particularly when network conditions are dynamic. This work also showcases the effectiveness of DRL in meeting multiple network objectives simultaneously, for instance, maximizing throughput of users as well as fairness of throughput among them.

The efficient management of radio resources in today's home or residential Wi-Fi networks is still an open research question. Due to the chaotic and unplanned deployment of Wi-Fi Access Points (APs) and the fact that all APs are managed individually by their owners, home Wi-Fi networks suffer from performance degradation due to contention and interference. In this paper we present and showcase a distributed radio channel assignment scheme, implemented using the ResFi platform, in which neighboring home Wi-Fi APs cooperate with each other in order to negotiate radio channel selection by taking into account the instantaneous network load of neighboring APs (two hop neighborhood). While the information about the network load can be directly exchanged between ResFi-enabled APs using the ResFi out-of-band Internet control channel, the information about the network load of co-located non-ResFi APs first needs to be estimated passively by monitoring the data traffic on the radio channel. The presented demonstrator of the approach is implemented using commodity hardware and enables the audience to observe the distributed channel adaptation in real-time.

Combining the high transmission speed of light fidelity (LiFi) and the ubiquitous coverage of wireless fidelity (WiFi), hybrid LiFi and WiFi network (HLWNet) has attracted intensive research interest. While such a network can boost the system capacity, it faces a challenging issue in handover since the coverage areas of LiFi and WiFi completely overlap each other. Also, LiFi has a relatively short coverage range from a single access point (AP). As a result, HLWNets are susceptible to frequent handovers, in terms of both handovers between LiFi and WiFi and handovers between two LiFi APs. Therefore handover skipping was introduced, which enables handovers between two non-adjacent APs. Conventional handover skipping schemes rely on knowledge about the user's trajectory, and are difficult to implement in practice. Alternatively, the rate of change in reference signal received power (RSRP) can be used to reflect the user's moving direction. Based on this, an adaptive handover scheme is proposed in this paper, which varies the user's network preference according to the user's speed. Results show that in comparison with the standard and trajectory-based schemes, the proposed approach can improve user throughput by up to about 120% and 30%, respectively.

Assuring the required quality of service, despite the growing volume and variety of Internet of Things (IoT) traffic, has remained an immense challenge in the popular WiFi networks. The IoT devices using short TCP flows often attain very different levels of service due to the complicated interactions between the transport layer protocol and the shared dynamic wireless medium. We develop in this paper a novel queue management policy by using a transient model to capture the interactions of IoT (short TCP) flows over traditional traffic in WiFi networks. Based on the Markov regenerative processes coupled with fluid model, we discover that the adaptive admission control (AAC) mechanism at the WiFi access point (AP) improves the response time and fairness of IoT traffic over the lossy WiFi links. To this end, under the proposed AAC, packets are admitted into the AP in such a way that the fractions of packets in the AP buffer belonging to the different IoT devices are balanced resulting in a comparable level of service. We furthermore prove the stability of the system dynamics under AAC which provides important practical insights in designing home WiFi IoT system.

Recent IT technology trends are confronted with a new technological phenomenon. It is the intersection of technology and liberal arts. That is, IT convergence. Although the convergence trends will keep going in the future, there is the Internet of Things which are a new priming to accelerate these trends. A multitude of IoT devices will transmit various information through Internet, and the main channel of information flows will be a Wi-Fi networks. However, current Wi-Fi technology isn’t prepared to accommodate these data yet. Therefore, we propose the Efficient Data Forwarding Scheme (EDFS) to solve these problems. The proposed EDFS assigns a proper transmission opportunity (TXOP) to the IoT devices for supporting efficient data transmission. In the EDFA, Wi-Fi AP (Access Point) gives a calculated or pre-defined TXOP, which is suitable for the data transmission attributes of IoT device. Due to the fact that the data transmission feature of IoT devices isn’t the real-time traffic in the most cases. Based on the EDFA, a Wi-Fi AP can not only assign an efficient TXOP to a device, but also support various IoT device requirements within the Wi-Fi hotspot. We compared it with legacy Wi-Fi transmission scheme to prove the outperforming results by using the OPNet simulator.Keywords: Data Forwarding, Internet of Things, TXOP, Wi-Fi