One of the crucial factors in improving the localization performance of an Ultra-wideband (UWB) downlink time-difference-of-arrival (DL-TDOA) system is the optimal location planning and cluster assignment of the UWB anchors. Prior research takes into account the dilution-of-precision (DOP) or the anchor-user device channel conditions, to solve the anchor location planning. However they do not consider the joint cluster assignment problem, and thus do not include the DL-TDOA cluster-specific metrics to optimize the anchor locations. This often leads to intra-cluster anchor-anchor clock offset related errors affecting the overall performance of DL-TDOA localization. In this paper, we propose a novel joint anchor location planning and cluster assignment algorithm for DL-TDOA. We not only take into account the anchor-user device channel conditions and the DOP, but also the channel conditions between anchors of the same cluster, which may lead to anchor-anchor clock offset based ranging errors. The simulation results show that our proposed algorithm achieves lower average localization error compared to baseline methods derived from prior research, as well as lower values of anchor-anchor clock offset based error, while having a fairly low computational complexity.

The growing commercial interest in an indoor location-based service (LBS) has stimulated the development of diverse indoor positioning systems. However, despite extensive researches on LBS in both industry and academy, the deployment of huge infrastructure for high localization accuracy and the absence of the killer app place limitations on the widespread of LBS. To resolve this problems, Ultra-wideband (UWB) is emerging as a promising solution to realize LBS thanks to its outstanding and robust localization performance. Based on the extremely high localization accuracy, we propose the remote control system, termed Wand, to control target devices without UWB capability with the help of two nearby UWB devices. Wand exploits UWB ranging results and inertial measurement unit sensor embedded in smartphones. We implemented Wand on Samsung Galaxy Note20 Ultra 5G (i.e., SM-N986B) to show the feasibility and applicability, and conducted real-world experiments to evaluate its performance. Wand achieves 33 and 62 cm localization accuracy in 2-D and 3-D, respectively. In addition, Wand correctly recognizes target devices with 94.7% accuracy.

The growing commercial interest in indoor localization-based services has stimulated the development of many indoor positioning systems. Despite extensive research on localization, system requirements, such as site survey, user intervention, or specific hardware/software, place limitations on the widespread deployment of localization. To overcome these limitations, we propose a path estimation and localization system for indoor environments, termedPYLON, that runs on a smartphone and a server without any human intervention.PYLONuses an actual floor plan and measurements from widely deployed WiFi access points (APs) and Bluetooth Low Energy (BLE) beacons to estimate the user’s path. It creates virtual rooms according to received signal strength indicator (RSSI) values and matches them to actual rooms in the real-world floor plan. After room mapping,PYLONuses door passing times to precisely refine a user’s estimated path. Unlike conventional path estimation and localization systems,PYLONworks independently of device types. We implementPYLONon five Android smartphones and conduct evaluation with three users in an office building. Our experimental results show thatPYLONachieves 97 percent floor plan mapping accuracy with a localization error of 1.42 m.

Internet of things (IoT) is an indispensable paradigm in today's industrial change. IoT interconnects appliances around us through the Internet, and user's private information is deeply placed inside the network. Nonetheless, security vulnerabilities in IoT have not been addressed appropriately so far due to various reasons. Even in the initial WiFi connection procedures of IoT devices, WiFi credentials can be leaked, making the WiFi access point (AP) a hacking path. We propose a secure connection scheme, termed PUP, that aims to securely and quickly connect an IoT device to the AP in proximity while improving the user experience. PUP shows perfect security performance, enabling connection time within 11 s.

Video multicast over wireless local area network (WLAN) has been gaining attraction for applications sharing a venue-specific common video with multiple users. However, wireless multicast is limited by a receiver that has the weakest communication link to the source. Collaborative relaying could overcome this challenge by enabling selected receiver nodes to relay the packets from the source to other receivers. We propose EV-CAST, an interference and energy-aware video multicast system using collaborative relays, which entails (i) online topology management based on interference-aware link characterization, (ii) joint selection of relay nodes and transmission parameters, and (iii) polling-based relay protocol. Our proposed algorithm, the core of EV-CAST, judiciously selects the relay nodes and transmission parameters in consideration of interference, battery status, and spatial reuse. Our prototype-based experiment results demonstrate that EV-CAST enhances video multicast delivery under various network scenarios. EV-CAST enables 2x more nodes to achieve a target video packet loss ratio with 0.59x shorter airtime than the state-of-the-art video multicast scheme.

Recently, WiFi has become irreplaceable wireless technology to support increasing mobile applications and data traffic. While today's smartphone users frequently use WiFi on the move, smartphone does not perform WiFi handoff to nearby access points (APs) even if signal quality is severely degraded. Such undesired operation of smartphone is known as sticky client problem. In this work, we focus on resolving sticky client problem through fast WiFi handoff. First, we analyze the causes of sticky client problem based on experiments with commercial Android smartphones by focusing on WiFi scanning and handoff operations. Note that smartphones and state-of-the-art APs are equipped with Bluetooth Low Energy (BLE), we propose a practical solution, called BLEND, utilizing BLE to enable fast WiFi handoff. Smartphone acquires information about nearby APs through BLE advertising packet sent by APs, and then judiciously performs WiFi handoff. We implement BLEND on Android smartphones and demonstrate that BLEND achieves up to 61% and 111% higher throughput and video bitrate, respectively, compared with a commercial Android application. We also validate that BLEND operates on smartphone with negligible energy overhead.

Multicast is a major solution in supporting the explosive growth of the wireless video traffic demand. Also, power saving is a key technology to extend battery life of mobile devices. To meet both purposes, IEEE 802.11 wireless local area network (WLAN) supports power save mode (PSM) for station (STA) while receiving multicast packets. According to recent studies, off-the-shelf chipsets configured to use PSM show un-desired functions, thus resulting in many multicast packet losses. From our extensive measurement, we also verify degradation of multicast performance with widely-used off-the-shelf chipsets using PSM, and present previously-unknown undesired functions. Without modification of the chipsets, STA in PSM cannot enjoy reliable multicast service. Given this, we develop a practical and readily-applicable AP-side solution, called ACT-AP, which avoids multicast packet losses by preventing STA from operating in PSM. Our prototype implementation with off-the-shelf chipsets demonstrates that ACT-AP improves packet delivery ratio by up to 216% with little additional protocol overhead. To our best knowledge, ACT-AP is the first practical effort to support multicast to real devices with undesired functions in PSM.

Multi-rate forward erasure correction (FEC)-applied wireless multicast enables reliable and efficient video multicast with intelligent selection of physical (PHY) layer data rate and FEC rate. The optimal PHY/FEC rates depend on the cause of the packet losses. However, previous approaches select the PHY/FEC rates by considering only channel errors even when interference is also a major source of packet losses. We propose InFRA, an interference-aware PHY/FEC rate adaptation framework that (i) infers the cause of the packet losses based on received signal strength indicator (RSSI) and cyclic redundancy check (CRC) error notifications, and (ii) determines the PHY/FEC rates based on the cause of packet losses. Our prototype implementation with off-the-shelf chipsets demonstrates that InFRA enhances the multicast delivery under various network scenarios. InFRA enables 2.3x and 1.8x more nodes to achieve a target video packet loss rate with a contention interferer and a hidden interferer, respectively, compared with the state-of- the-art PHY/FEC rate adaptation scheme. To our best knowledge, InFRA is the first work to take the impact of interference into account for the PHY/FEC rate adaptation.