Towards Personal Virtual Traffic Lights

Traffic control management at intersections, a challenging and complex field of study, aims to strike a balance between safety and efficient traffic control. Nowadays, traffic control at intersections is typically done by traffic light systems which are not optimal and exhibit several drawbacks, such as poor efficiency and real-time adaptability. With the advent of Intelligent Transportation Systems (ITS), vehicles are being equipped with state-of-the-art technology, enabling cooperative decision-making which will certainly overwhelm the available traffic control systems. This solution strongly penalizes users without such capabilities, namely pedestrians, cyclists, and other legacy vehicles. Therefore, in this work, a prototype based on an alternative technology to the standard vehicular communications, Bluetooth Low Energy (BLE), is presented. The proposed framework aims to integrate legacy and modern vehicular communication systems into a cohesive management system. In this framework, the movements of users at intersections are managed by a centralized controller which, through the use of networked retransmitters deployed at intersections, broadcasts alerts and virtual light signalization orders. Users receive the aforementioned information on their own smart devices, discarding the need for dedicated light signalization infrastructures. Field tests, carried out with a real-world implementation, validate the correct operation of the proposed framework. Document type: Articl

Control Led Through Internet Based on Nodemcu with Blynk Application

   Internet (interconnected-networking) is a series of computers that are connected globally in several circuits and use TCP / IP as packet exchange communication protocol. The internet as part of the technological development that is very rapidly developing in people\u27s lives today has been able to be used as a medium of communication and control of devices from a distance as long as they are still connected to each other. The internet is like virtual threads that connect with one another, forward data and convey data from one point to another. However, along with the development of increasingly advanced science and technology, the internet is no longer just to connect between humans but also to control between any object that can be connected. In this study there were 3 (three) problems and 3 (three) problem solving methods Electronic device control in the form of LED lights using an IoT platform that is open source. This study uses the NodeMCU module as a station, which will be controlled by the Blynk application with an internet connection. In this study using the NodeMCU module as a station, which will be controlled by the Blynk application by connecting to the internet

User Experience Enhancement on Smartphones using Wireless Communication Technologies

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 박세웅.Recently, various sensors as well as wireless communication technologies such as Wi-Fi and Bluetooth Low Energy (BLE) have been equipped with smartphones. In addition, in many cases, users use a smartphone while on the move, so if a wireless communication technologies and various sensors are used for a mobile user, a better user experience can be provided. For example, when a user moves while using Wi-Fi, the user experience can be improved by providing a seamless Wi-Fi service. In addition, it is possible to provide a special service such as indoor positioning or navigation by estimating the users mobility in an indoor environment, and additional services such as location-based advertising and payment systems can also be provided. Therefore, improving the user experience by using wireless communication technology and smartphones sensors is considered to be an important research field in the future. In this dissertation, we propose three systems that can improve the user experience or convenience by usingWi-Fi, BLE, and smartphones sensors: (i) BLEND: BLE beacon-aided fast Wi-Fi handoff for smartphones, (ii) PYLON: Smartphone based Indoor Path Estimation and Localization without Human Intervention, (iii) FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance. First, we propose fast handoff scheme called BLEND exploiting BLE as secondary radio. We conduct detailed analysis of the sticky client problem on commercial smartphones with experiment and close examination of Android source code. We propose BLEND, which exploits BLE modules to provide smartphones with prior knowledge of the presence and information of APs operating at 2.4 and 5 GHz Wi-Fi channels. BLEND operating with only application requires no hardware and Android source code modification of smartphones.We prototype BLEND with commercial smartphones and evaluate the performance in real environment. Our measurement results demonstrate that BLEND significantly improves throughput and video bitrate by up to 61% and 111%, compared to a commercial Android application, respectively, with negligible energy overhead. Second, we design a path estimation and localization system, termed PYLON, which is plug-and-play on Android smartphones. PYLON includes a novel landmark correction scheme that leverages real doors of indoor environments consisting of floor plan mapping, door passing time detection and correction. It operates without any user intervention. PYLON relaxes some requirements for localization systems. It does not require any modifications to hardware or software of smartphones, and the initial location of WiFi APs, BLE beacons, and users. We implement PYLON on five Android smartphones and evaluate it on two office buildings with the help of three participants to prove applicability and scalability. PYLON achieves very high floor plan mapping accuracy with a low localization error. Finally, We design a fully-automated navigation system, termed FINISH, which addresses the problems of existing previous indoor navigation systems. FINISH generates the radio map of an indoor building based on the localization system to determine the initial location of the user. FINISH relaxes some requirements for current indoor navigation systems. It does not require any human assistance to provide navigation instructions. In addition, it is plug-and-play on Android smartphones. We implement FINISH on five Android smartphones and evaluate it on five floors of an office building with the help of multiple users to prove applicability and scalability. FINISH determines the location of the user with extremely high accuracy with in one step. In summary, we propose systems that enhance the users convenience and experience by utilizing wireless infrastructures such as Wi-Fi and BLE and various smartphones sensors such as accelerometer, gyroscope, and barometer equipped in smartphones. Systems are implemented on commercial smartphones to verify the performance through experiments. As a result, systems show the excellent performance that can enhance the users experience.1 Introduction 1 1.1 Motivation 1 1.2 Overview of Existing Approaches 3 1.2.1 Wi-Fi handoff for smartphones 3 1.2.2 Indoor path estimation and localization 4 1.2.3 Indoor navigation 5 1.3 Main Contributions 7 1.3.1 BLEND: BLE Beacon-aided Fast Handoff for Smartphones 7 1.3.2 PYLON: Smartphone Based Indoor Path Estimation and Localization with Human Intervention 8 1.3.3 FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance 9 1.4 Organization of Dissertation 10 2 BLEND: BLE Beacon-Aided FastWi-Fi Handoff for Smartphones 11 2.1 Introduction 11 2.2 Related Work 14 2.2.1 Wi-Fi-based Handoff 14 2.2.2 WPAN-aided AP Discovery 15 2.3 Background 16 2.3.1 Handoff Procedure in IEEE 802.11 16 2.3.2 BSS Load Element in IEEE 802.11 16 2.3.3 Bluetooth Low Energy 17 2.4 Sticky Client Problem 17 2.4.1 Sticky Client Problem of Commercial Smartphone 17 2.4.2 Cause of Sticky Client Problem 20 2.5 BLEND: Proposed Scheme 21 2.5.1 Advantages and Necessities of BLE as Secondary Low-Power Radio 21 2.5.2 Overall Architecture 22 2.5.3 AP Operation 23 2.5.4 Smartphone Operation 24 2.5.5 Verification of aTH estimation 28 2.6 Performance Evaluation 30 2.6.1 Implementation and Measurement Setup 30 2.6.2 Saturated Traffic Scenario 31 2.6.3 Video Streaming Scenario 35 2.7 Summary 38 3 PYLON: Smartphone based Indoor Path Estimation and Localization without Human Intervention 41 3.1 Introduction 41 3.2 Background and Related Work 44 3.2.1 Infrastructure-Based Localization 44 3.2.2 Fingerprint-Based Localization 45 3.2.3 Model-Based Localization 45 3.2.4 Dead Reckoning 46 3.2.5 Landmark-Based Localization 47 3.2.6 Simultaneous Localization and Mapping (SLAM) 47 3.3 System Overview 48 3.3.1 Notable RSSI Signature 49 3.3.2 Smartphone Operation 50 3.3.3 Server Operation 51 3.4 Path Estimation 52 3.4.1 Step Detection 52 3.4.2 Step Length Estimation 54 3.4.3 Walking Direction 54 3.4.4 Location Update 55 3.5 Landmark Correction Part 1: Virtual Room Generation 56 3.5.1 RSSI Stacking Difference 56 3.5.2 Virtual Room Generation 57 3.5.3 Virtual Graph Generation 59 3.5.4 Physical Graph Generation 60 3.6 Landmark Correction Part 2: From Floor Plan Mapping to Path Correction 60 3.6.1 Candidate Graph Generation 60 3.6.2 Backbone Node Mapping 62 3.6.3 Dead-end Node Mapping 65 3.6.4 Final Candidate Graph Selection 66 3.6.5 Door Passing Time Detection 68 3.6.6 Path Correction 70 3.7 Particle Filter 71 3.8 Performance Evaluation 73 3.8.1 Implementation and Measurement Setup 73 3.8.2 Step Detection Accuracy 77 3.8.3 Floor Plan Mapping Accuracy 77 3.8.4 Door Passing Time 78 3.8.5 Walking Direction and Localization Performance 81 3.8.6 Impact of WiFi AP and BLE Beacon Number 84 3.8.7 Impact of Walking Distance and Speed 84 3.8.8 Performance on Different Areas 87 3.9 Summary 87 4 FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance 91 4.1 Introduction 91 4.2 Related Work 92 4.2.1 Localization-based Navigation System 92 4.2.2 Peer-to-peer Navigation System 93 4.3 System Overview 93 4.3.1 System Architecture 93 4.3.2 An Example for Navigation 95 4.4 Level Change Detection and Floor Decision 96 4.4.1 Level Change Detection 96 4.5 Real-time navigation 97 4.5.1 Initial Floor and Location Decision 97 4.5.2 Orientation Adjustment 98 4.5.3 Shortest Path Estimation 99 4.6 Performance Evaluation 99 4.6.1 Initial Location Accuracy 99 4.6.2 Real-Time Navigation Accuracy 100 4.7 Summary 101 5 Conclusion 102 5.1 Research Contributions 102 5.2 Future Work 103 Abstract (In Korean) 118 감사의 글Docto

Back of Queue Warning and Critical Information Delivery to Motorists

Back-of-queue crashes are one of the main sources for fatal accidents on U.S. highways. A variety of factors including low visibility, slippery road surface, and driver distraction/drowsiness during highway cruising, all contribute to this type of fatal crashes. Thus, it is very important to improve the driver’s situational awareness before they approach traffic queues on highways. In this project, we develop a prototype in-vehicle back-of-queue alerting system that is based on the probe vehicle data from INDOT. Speed changes among different road segments are used to identify slow traffic queues, which are compared with vehicle locations and moving directions to detect potential back-of-queue crashes. This prototype system is designed to issue alerting messages to drivers approaching the highway traffic queues via an Android-based smartphone app and an Android Auto device. The performance of this system has been evaluated using the driving simulator and a limited number of on-road test runs. The results showed the effectiveness and benefits of this prototype system

An Investigation of Power Saving and Privacy Protection on Smartphones

With the advancements in mobile technology, smartphones have become ubiquitous in people\u27s daily lives and have greatly facilitated users in many aspects. For a smartphone user, power saving and privacy protection are two important issues that matter and draw serious attentions from research communities. In this dissertation, we present our studies on some specific issues of power saving and privacy protection on a smartphone. Although IEEE 802.11 standards provide Power Save Mode (PSM) to help mobile devices conserve energy, PSM fails to bring expected benefits in many real scenarios. We define an energy conserving model to describe the general PSM traffic contention problem, and propose a solution called HPSM to address one specific case, in which multiple PSM clients associate to a single AP. In HPSM, we first use a basic sociological concept to define the richness of a PSM client based on the link resource it consumes. Then we separate these poor PSM clients from rich PSM clients in terms of link resource consumption, and favor the former to save power when they face PSM transmission contention. Our evaluations show that HPSM can help the poor PSM clients effectively save power while only slightly degrading the rich\u27s performance in comparison to the existing PSM solutions. Traditional user authentication methods using passcode or finger movement on smartphones are vulnerable to shoulder surfing attack, smudge attack, and keylogger attack. These attacks are able to infer a passcode based on the information collection of user\u27s finger movement or tapping input. as an alternative user authentication approach, eye tracking can reduce the risk of suffering those attacks effectively because no hand input is required. We propose a new eye tracking method for user authentication on a smartphone. It utilizes the smartphone\u27s front camera to capture a user\u27s eye movement trajectories which are used as the input of user authentication. No special hardware or calibration process is needed. We develop a prototype and evaluate its effectiveness on an android smartphone. Our evaluation results show that the proposed eye tracking technique achieves very high accuracy in user authentication. While LBS-based apps facilitate users in many application scenarios, they raise concerns on the breach of privacy related to location access. We perform the first measurement of this background action on the Google app market. Our investigation demonstrates that many popular apps conduct location access in background within short intervals. This enables these apps to collect a user\u27s location trace, from which the important personal information, Points of Interest (PoIs), can be recognized. We further extract a user\u27s movement pattern from the PoIs, and utilize it to measure the potential privacy breach. The measurement results also show that using the combination of movement pattern related metrics and the other PoI related metrics can help detect the privacy breach in an earlier manner than using either one of them alone. We then propose a preliminary solution to properly handle these location requests from background