Wi-Fi MAC Research Articles

Tracking people across ultra populated indoor spaces by matching unreliable Wi-Fi signals with disconnected video feeds

Tracking in dense indoor environments where several thousands of people move around is an extremely challenging problem. In this paper, we present a system — DenseTrack for tracking people in such environments. DenseTrack leverages data from the sensing modalities that are already present in these environments — Wi-Fi (from enterprise network deployments) and Video (from surveillance cameras). We combine Wi-Fi information with video data to overcome the individual errors induced by these modalities. More precisely, the locations derived from video are used to overcome the localization errors inherent in using Wi-Fi signals where precise Wi-Fi MAC IDs are used to locate the same devices across different levels and locations inside a building. Typically, localization in dense environments is a computationally expensive process when done with just video data; hence hard to scale. DenseTrack combines Wi-Fi and video data to improve the accuracy of tracking people that are represented by video objects from non-overlapping video feeds. DenseTrack is a scalable and device-agnostic solution as it does not require any app installation on user smartphones or modifications to the Wi-Fi system. At the core of DenseTrack, is our algorithm — inCremental Association of Independent Variables under Uncertainty (CAIVU). CAIVU is inspired by the multi-armed bandits model and is designed to handle various complex features of practical real-world environments. CAIVU matches the devices reported by an off-the-shelf Wi-Fi system using connectivity information to specific video blobs obtained through a computationally efficient analysis of video data. By exploiting data from heterogeneous sources, DenseTrack offers an effective real-time solution for individual tracking in heavily populated indoor environments. We emphasize that no other previous system targeted nor was validated in such dense indoor environments. We tested DenseTrack extensively using both simulated data, as well as two real-world validations using data from an extremely dense convention center and a moderately dense university environment. Our simulation results show that DenseTrack achieves an average video-to-Wi-Fi matching accuracy of up to 90% in dense environments with a matching latency of 60 s on the simulator. When tested in a real-world extremely dense environment with over 500,000 people moving between different non-overlapping camera feeds, DenseTrack achieved an average match accuracy of 83% to within a 2-people distance with an average latency of 48 s.

Time-Sensitive Networking to Improve the Performance of Distributed Functional Safety Systems Implemented over Wi-Fi.

Industry 4.0 has significantly improved the industrial manufacturing scenario in recent years. The Industrial Internet of Things (IIoT) enables the creation of globally interconnected smart factories, where constituent elements seamlessly exchange information. Industry 5.0 has further complemented these achievements, as it focuses on a human-centric approach where humans become part of this network of things, leading to a robust human-machine interaction. In this distributed, dynamic, and highly interconnected environment, functional safety is essential for adequately protecting people and machinery. The increasing availability of wireless networks makes it possible to implement distributed and flexible functional safety systems. However, such networks are known for introducing unwanted delays that can lead to safety performance degradation due to their inherent uncertainty. In this context, the Time-Sensitive Networking (TSN) standards present an attractive prospect for enhancing and ensuring acceptable behaviors. The research presented in this paper deals with the introduction of TSN to implement functional safety protocols for wireless networks. Among the available solutions, we selected Wi-Fi since it is a widespread network, often considered and deployed for industrial applications. The introduction of a reference functional safety protocol is detailed, along with an analysis of how TSN can enhance its behavior by evaluating relevant performance indexes. The evaluation pertains to a standard case study of an industrial warehouse, tested through practical simulations. The results demonstrate that TSN provides notable advantages, but it requires meticulous coordination with the Wi-Fi MAC layer protocol to guarantee improved performance.

Packets-to-Prediction: An Unobtrusive Mechanism for Identifying Coarse-Grained Sleep Patterns with WiFi MAC Layer Traffic.

A good night's sleep is of the utmost importance for the seamless execution of our cognitive capabilities. Unfortunately, the research shows that one-third of the US adult population is severely sleep deprived. With college students as our focused group, we devised a contactless, unobtrusive mechanism to detect sleep patterns, which, contrary to existing sensor-based solutions, does not require the subject to put on any sensors on the body or buy expensive sleep sensing equipment. We named this mechanism Packets-to-Predictions(P2P) because we leverage the WiFi MAC layer traffic collected in the home and university environments to predict "sleep" and "awake" periods. We first manually established that extracting such patterns is feasible, and then, we trained various machine learning models to identify these patterns automatically. We trained six machine learning models-K nearest neighbors, logistic regression, random forest classifier, support vector classifier, gradient boosting classifier, and multilayer perceptron. K nearest neighbors gave the best performance with 87% train accuracy and 83% test accuracy.

Introducing Reinforcement Learning in the Wi-Fi MAC Layer to Support Sustainable Communications in e-Health Scenarios

Introducing Reinforcement Learning in the Wi-Fi MAC Layer to Support Sustainable Communications in e-Health Scenarios

MAC address de-randomization for WiFi device counting: Combining temporal- and content-based fingerprints

To preserve people privacy and prevent device (and people) tracking, WiFi MAC address randomization is been introduced by an ever increasing number of operating systems. Accordingly, mobile devices make use of different virtual addresses over time so that not a single fixed factory address is used that may identify a specific user. This has the consequence that it is not even possible to extract anonymous information on people mobility by analyzing WiFi traffic traces, which would be useful for many purposes (e.g., counting the number of people in a mass transport vehicle).To address this issue, in this paper we propose a novel MAC address de-randomization algorithm which groups specific messages (i.e., the Probe Requests) generated by the same physical device. With respect to past works, we consider a combination of the features that have been previously considered in isolation, which are associated to the content and length of the optional fields conveyed in the sent frames and the rate at which the frames are numbered over time. These features are then used by density-based clustering algorithms (i.e., DBSCAN, OPTICS, HDBSCAN) to group frames sent by the same device. Additionally, we consider the presence of pseudo-random MAC addresses, which are those that do not change every frame but only when the emitting device switches on and off the WiFi interface. To this, we have developed an heuristic to detect these sequences of frames so as to improve the algorithm efficacy. Experiments have been initially performed in a controlled environment where we reached an accuracy close to 96%. Then, experiments in a real scenario have been conducted where the people taking the bus when moving in an urban area have been counted; in such a scenario an average accuracy of 75% has been obtained.

Analysis of road traffic pattern changes due to activity restrictions during COVID-19 pandemic in Chennai

ABSTRACT In the absence of pharmaceutical interventions for the Novel Coronavirus (COVID-19), countries have taken drastic steps like quarantine, prohibit large-scale gatherings, limited transport, social distancing, curfews, and lockdowns to curtail the spread of the virus. In light of these events, the current study attempts to understand the short-term changes in road traffic patterns, using data from two Wi-Fi MAC Scanners deployed at strategic locations in Chennai, India. The results indicate that the road traffic activities significantly reduced due to the restrictions in non-essential trips, workplace suspensions, and strict surveillance during lockdowns. However, as the lockdown rules eased, the road traffic activities began to recover. It is found that complete closedown is most effective in reducing road travel activity, but ad-hoc short duration complete closedowns may only yield temporary benefits. Also, extended lockdowns without proper enforcement may be ineffective since the public appeared to ignore the advisory after a while.

CEDAR: A Cost-Effective Crowdsensing System for Detecting and Localizing Drones

The increasing popularity of drones is bringing many public security and privacy breach issues, such as smuggling, intrusion, and illegal surveillance. Traditional approaches to detecting and localizing drones such as radar and computer vision incur high costs and hence are not desirable for large-scale applications. In this paper, we propose a cost-effective crowdsensing system named CEDAR to achieve such a goal. Specifically, we introduce a novel way of detecting drones by smartphones, exploiting the fact that most drones adopt Wi-Fi for communications with ground control stations. We design an efficient detection algorithm that takes advantage of historical Wi-Fi beacon information and MAC address encoding mechanisms used by drone manufacturers. Using received signal strength, we can also localize the detected drones. Further, to encourage participants’ involvement, we design an incentive mechanism based on online auction that guarantees truthfulness and consumer sovereignty. CEDAR can be directly applied to multiple drone scenarios. We implement the system based on Android for the client and Spring, Spring MVC, and Mybatis (SSM) for the centralized platform that supports scalability and hierarchical structure, and enables the coordination between clients and the platform. We perform extensive experiments to validate our analysis. Particularly, the detection rate in the experiments reaches 86.7 percent even without any prior information about drones.

Bus passenger counts using Wi-Fi signals: some cautionary findings

Analisa-se a viabilidade de pesquisas sobre usuários de ônibus com base na detecção de endereços MAC WiFi de dispositivos portáteis. A motivação para o estudo decorre da aparente contradição entre casos de sucesso publicados na literatura e resultados de experimentos de campo que realizamos. Requisitos para identificação adequada de passageiros de ônibus são usados como base para avaliar as capacidades do hardware e software de detecção comumente disponíveis. Mais especificamente, os intervalos de tempo decorridos entre as detecções do mesmo dispositivo são tomados como requisito para a determinação do estado do portador do dispositivo e, portanto, a identificação deste como passageiro. Por exemplo, ao realizar pesquisas de embarque e desembarque com equipamentos de detecção instalados a bordo, é necessário que várias detecções ocorram logo após o embarque do passageiro e antes do desembarque, permitindo assim uma estimativa precisa da origem e destino da viagem. Resultados experimentais em ensaios controlados e não controlados indicam que os componentes disponíveis no mercado com software de código aberto podem não fornecer detecções bem-sucedidas. No experimento controlado, encontramos tempos de 40 s para a primeira detecção de 86% dos dispositivos e uma média de 80 s para a segunda detecção de dispositivos. Para o experimento não controlado de viagens em ônibus com carregamento médio, foram encontradas diferenças significativas entre as contagens manuais e os dispositivos detectados. Como resultado dessas observações empíricas, recomenda-se uma avaliação cuidadosa dos esquemas de detecção existentes usados nas pesquisas de número de passageiros.

Fast surveillance video indexing & retrieval with WiFi MAC address tagging

<span>Conventional public safety surveillance video camera systems required 24/7 monitoring of security officers with video wall display installed in the control room. When a crime or incident is reported, all the recorded surveillance video streams nearby the incident area are playback simultaneously on video wall to help locate the target person. The security officers can fast forward the video playback to speed up the video search, but it requires massive manpower if there are hundreds of video streams required to be examined on the video wall. One of the possible solutions is through a suitable video indexing and retrieval technique to prioritize the video frames that need to be processed. This paper presents a WiFi sniffer enabled surveillance camera, with 3-stage WiFi frame inspection filter and the use of collected WiFi signal strength for filtering, to tag the collected WiFi MAC addresses to the surveillance video frames according to the time of the MAC address is sniffed. Additional metadata (WiFi MAC address of smartphone) collected during the occurrence of the incident can be used to prioritize the retrieving of surveillance video frames for subsequent image processing. </span>

Estimation of bus passengers’ waiting time at a coach terminal with Wi-Fi MAC addresses

Abstract The purpose of this study is to develop a method to estimate duration of stay at transit facilities by detecting Wi-Fi MAC addresses. The validation survey was conducted at one of the largest highway bus terminals, “Busta Shinjuku”, in Tokyo, Japan. The data is compared to interview survey results obtained during the same period for verification. The estimated staying duration was smaller than the one obtained by interview surveys. This implies that Wi-Fi survey possibly observed through traffic as well as the staying travelers.

Network Coordination Function for uplink traffic steering in tightly coupled LTE Wi-Fi networks

Tight coupling of LTE and Wi-Fi networks is accomplished by binding their protocol stacks. LTE Wi-Fi radio level integration with IPSec tunnel (LWIP) corresponds to realizing this binding at IP layer. A collocated deployment of LWIP enables greater flexibility in utilizing the channel efficiently. With the advent of bandwidth-hungry smartphone Apps and IoT applications, the cellular uplink resources become highly demanding. This enforces Wi-Fi to support efficient uplink transmissions since the uplink transmissions through Wi-Fi suffers high contention because of distributed nature of Wi-Fi MAC. In order to improve Wi-Fi channel utilization by leveraging the potential of LWIP in controlling and coordinating the transmissions through LTE and Wi-Fi links, we introduce Network Coordination Function (NCF) in LWIP. The proposed NCF focuses on coordinating the uplink transmissions through Wi-Fi in a network with high load. NCF enhances the channel utilization of Wi-Fi network by regulating the packet arrival rate to the Wi-Fi link and also by revamping medium access techniques at the Wi-Fi interface of users associated with LWIP node. NCF is composed of four different uplink traffic steering algorithms with diverse objectives which improve Wi-Fi channel utilization by (i) minimizing collisions among LWIP users, (ii) increasing transmission opportunities for Wi-Fi users that are connected to legacy Wi-Fi APs operating on the same channel, and (iii) ensuring fairness for both LWIP and Wi-Fi users. Interestingly, NCF has not only improved the throughput of LWIP users but also the throughput of Wi-Fi users. Simulation experiments reveal that NCF has reduced collisions in the Wi-Fi uplink by 13–53% and improved throughput by 10–37% as compared to Wi-Fi offloading and Distributed Coordination Function (DCF).

Coexistence Wi-Fi MAC Design for Mitigating Interference Caused by Collocated Bluetooth

A non-collaborative coexistence mechanism for wireless-fidelity (Wi-Fi) and bluetooth (BT) systems based on dynamic packet fragmentation is proposed in this work. The basic idea is to adapt the packet length of Wi-Fi in the MAC layer such that the fragmented packet has a better chance to survive the interference from the nearby BT devices. We first develop an analytical model that specifies the information required by the Wi-Fi MAC layer to decide the best fragmentation strategy. Then, this model is extended to analyze the throughput and transmission delay of the Wi-Fi device. The analytical model is validated by computer simulation. Furthermore, it is demonstrated by simulation results that the proposed coexistence mechanism improves the performance of Wi-Fi in throughput and transmission delay significantly while relatively smaller performance improvement is observed for BT.

Edward Chung [ITS People

Profiles Professor Edward Chung, a researcher in the field of intelligent transportation systems. Dr. Chung’s research interests include development of algorithms for enhancing network erformance and capacity on motorways and arterial roads, traffic management using cooperative ITS (vehicle to infrastructure) and monitoring crowd and vehicle movements using Wi-Fi and Bluetooth MAC address.

Medium access in WiFi networks: strategies of selfish nodes [Applications Corner

This article provides a game theoretical analysis of the WiFi MAC protocol to understand the risks or the advantages offered by possible modifications of MAC functionalities implemented at the driver level.

Contention‐free approaches for WiFi MAC design for VoIP services: performance analysis and comparison

AbstractThe exponential growth in the demand of voice over internet protocol (VoIP) services along with the increasing demand for mobility in VoIP services has attracted great research efforts towards provisioning of VoIP services in IEEE 802.11‐based Wireless LANs (WiFi networks). We address one of the important research problems, namely, the quality of service (QoS)‐aware efficient silence suppression in the bursty voice traffic, for provisioning VoIP services in WiFi networks. The research works in the recent literature on silence suppression in voice calls have been surveyed categorising them on how the activity arrival is notified to the access point (AP). In most of the recent schemes, notification of uplink activity arrival is done through contention based medium access mechanisms such as the distributed coordination function (DCF). Contention‐based medium access causes non‐deterministic delays, therefore such schemes are not suited to voice traffic which require strict delay bound guarantees. This paper focuses on the schemes which do not use contention based approaches for silence suppression in voice traffic. Analytical performance evaluation and comparison of such schemes is carried out. Two very important performance metrics are modelled mathematically. One is the expected polling overhead time that the schedulers in these schemes can save per voice call during one voice activity cycle as compared to that in the round‐robin polling scheduler. The other is the expected unnecessary wireless channel access delay that a typical first talk‐spurt frame experiences due to the specific design of each scheme. The numerical results of this evaluation lead us to the conclusion whether or not and to what extent each of these schemes is viable. Copyright © 2008 John Wiley & Sons, Ltd.

Design of a TDD Multisector TDM MAC for the WiFiRe Proposal for Rural Broadband Access

The WiFiRe (WiFi Rural Extension) proposal for rural broadband access is being developed under the aegis of CEWIT. The system leverages the widely available, and highly cost-reduced, WiFi chipsets. However, only the physical layer from these chipsets is retained. A single base station carries several WiFi transceivers, each serving one sector of the cell, and all operating on the same WiFi channel in a time division duplex (TDD) manner. We replace the contention based WiFi MAC with a single-channel TDD multisector TDM MAC similar to the WiMax MAC. In this paper we discuss in detail the issues in designing such a MAC for the purpose of carrying packet voice telephony and for Internet access. The problem of determining the optimal spatial reuse is formulated and the optimal spatial reuse and the corresponding cell size is derived. Then the voice and data scheduler is designed. It is shown how throughput fairness can be implemented in the data scheduler. A capacity assessment of the system is also provided.