Ultra-wideband (UWB) has recently gained wide attention as a promising solution to realize proximity and location based services due to its extremely high localization accuracy. Currently, the most commercialized wireless technology for proximity and location based services is near-field communication (NFC). However, NFC has an extremely short communication range, and hence requires user intervention to access proximity and location services. By replacing NFC with UWB, users no longer need to tag their mobile devices for such services. In this paper, a UWB contactless gate (UCG) system is proposed that requires no interaction from users when they pass through gates in the subway or stadium. The protocol for the UCG system is proposed and implemented on commercial devices. The experiments for the UCG system also are conducted in the realworld environments, with accompanying results proving that the UCG system can operate without any user interaction.

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.

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.

Exploiting both LTE and Wi-Fi links simultaneously enhances the performance of video streaming services in a smartphone. However, it is challenging to achieve seamless and high quality video while saving battery energy and LTE data usage to prolong the usage time of a smartphone. In this paper, we propose REQUEST, a video chunk request policy for Dynamic Adaptive Streaming over HTTP (DASH) in a smartphone, which can utilize both LTE and Wi-Fi. REQUEST enables seamless DASH video streaming with near optimal video quality under given budgets of battery energy and LTE data usage. Through extensive simulation and measurement in a real environment, we demonstrate that REQUEST significantly outperforms other existing schemes in terms of average video bitrate, rebuffering, and resource waste.

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.

The increasing demand for ubiquitous wireless connectivity has led to the widespread of Wi-Fi networks. However, due to the limited coverage of Wi-Fi networks, mobile stations (STAs) need to frequently search for neighboring Wi-Fi access points (APs). Inevitable inefficiency occurs during the Wi-Fi scanning since the STA typically does not have any prior knowledge of the neighboring APs, thus leading to unnecessary waste of time and energy. In this paper, we propose BlueScan, a scheme to boost Wi-Fi scanning using collocated Bluetooth radio. BlueScan enhances Wi-Fi scanning with low power Bluetooth radio by identifying the operating channels and target beacon transmission times (TBTTs) of neighboring APs, thus changing the Wi-Fi scanning from a blind search to an intelligent search. We implement a prototype of BlueScan using Ubertooth platform, and evaluate its performance through real experiments. Our results demonstrate that BlueScan reduces the scanning delay up to 77% compared to legacy Wi-Fi scanning.

Dense Wi-Fi and Bluetooth (BT) environments become increasingly common so that the coexistence issue between Wi-Fi and BT is imperative to solve. In this paper, we propose BlueCoDE, a coordination scheme for multiple neighboring BT piconets, to make them collision-free and less harmful to Wi-Fi. BlueCoDE reuses BT's existing PHY and MAC design, thus making it practically feasible. We implement a prototype of BlueCoDE on Ubertooth One platform and corroborate the performance gain via analysis, NS-3 simulations, and prototype-based experiments. Our experimental results show that with merely 10 legacy BT piconets, neighboring Wi-Fi network becomes useless achieving under 1 Mb/s throughput, while BlueCoDE enables the Wi-Fi throughput always remain above 12 Mb/s. We expect BlueCoDE to be a breakthrough solution for coexistence in dense Wi-Fi and BT environments.

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.

Exploiting both LTE and Wi-Fi links simultaneously enhances the performance of video streaming services in a smartphone. However, it is challenging to achieve seamless and high quality video while saving battery energy and LTE data usage to prolong the usage time of a smartphone. In this paper, we propose REQUEST, a video chunk request policy for Dynamic Adaptive Streaming over HTTP (DASH) in a smartphone, which can utilize both LTE and Wi-Fi. REQUEST enables seamless DASH video streaming with near optimal video quality under given budgets of battery energy and LTE data usage. Through extensive simulation and measurement in a real environment, we demonstrate that REQUEST significantly outperforms other existing schemes in terms of average video bitrate, rebuffering, and resource waste.

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.

Full charge capacity (FCC) refers to the amount of charge a battery can hold. It is the fundamental property of smartphone batteries that diminishes as the battery ages and is charged/discharged. We investigate the behavior of smartphone batteries while charging and demonstrate that battery voltage and charging rate information can together characterize the FCC of a battery. We propose a new method for accurately estimating FCC without exposing low-level system details or introducing new hardware or system modules. We further propose and implement a collaborative FCC estimation technique that builds on crowd-sourced battery data. The method finds the reference voltage curve and charging rate of a particular smartphone model from the data and then compares with those of an individual device. After analyzing a large data set towards a crowd-sourced rate versus FCC model, we report that 55 percent of all devices and at least one device in 330 out of 357 unique device models lost some of their FCC. For some old device models, the median capacity loss exceeded 20 percent. The models further enable debugging the performance of smartphone charging. We propose an algorithm, called BatterySense, which utilizes crowd-sourced rate to detect abnormal charging performance, estimate FCC of the device battery, and detect battery changes.

Energy awareness of mobile devices with limited battery capacity can be achieved by embedding battery drain rate monitoring capability into the devices. With Li-ion battery, battery drain rate varies with temperature and battery aging, since they affect battery characteristics such as capacity and internal resistance. We develop BattTracker, an algorithm to estimate battery drain rate without knowing the exact capacity and internal resistance by incorporating the concept of effective resistance. BattTracker tracks the instantaneous battery drain rate with up to 0.5 second time granularity. Extensive evaluation with smartphones demonstrates that BattTracker accurately estimates the battery drain rate with less than 5% estimation error, thus enabling energy-aware operation of smartphones with finegrained time granularity. To the best of our knowledge, this is the first effort to estimate the instantaneous battery drain rate by considering both temperature and aging effects on the battery characteristics.

The emerging 60-GHz multigigabits per second wireless technology enables the streaming of high-quality “uncompressed” video, which has been impossible with other existing wireless technologies. To support such a resource-hungry uncompressed video streaming service with limited wireless resources, it is necessary to design efficient link adaptation policies selecting suitable transmission rates for the 60-GHz wireless channel environment, thus optimizing video quality and resource management. For proper design of the link adaptation policies, we propose a new metric, called expected peak signal-to-noise ratio (ePSNR), to numerically estimate the video streaming quality. By using the ePSNR as a criterion, we propose two link adaptation policies with different objectives considering unequal error protection (UEP). The proposed link adaptation policies attempt to 1) maximize the video quality for given wireless resources, or 2) minimize the required wireless resources, while meeting the video quality. From the link adaptation policies, we provide a distributed resource management scheme for multiple users to maintain satisfactory video streaming quality. Our extensive simulation results demonstrate that the newly proposed variable, i.e., ePSNR, well represents the level of video quality. It is also shown that the proposed link adaptation policies can enhance the resource efficiency while achieving acceptable quality of the video streaming.

Forward Error Correction (FEC) can be exploited to realize reliable video multicast over Wi-Fi with high video quality. We propose reliable video multicast over Wi-Fi networks with coordinated multiple Access Points (APs) to enhance video quality. By coordinating multiple APs, each AP can transmit entirely or partially different FEC-encoded packets so that a multicast receiver can benefit from both spatial and time diversities. The proposed schemes can enlarge the satisfactory video multicast region by exploiting the AP diversity, thus serving more multicast receivers located at cell edge with satisfactory video quality. We propose a resource-allocation algorithm for FEC-code rate adaptation, utilizing the limited wireless resource more efficiently while enhancing video quality. We also introduce the method for estimating the video packet delivery ratio after FEC decoding. The effectiveness of the proposed schemes is comparatively evaluated via extensive simulation and experimentation. The proposed schemes are observed to enhance the ratio of satisfied users by up to 37.1% compared to the conventional single AP multicast scheme.

We propose E$PA, an adaptive LTE/WiFi network interface activation algorithm with supporting system design and multi-attribute cost function for smartphones file transfer services (e.g., downloading a movie file). E$PA's cost function incorporates battery life (energy), data usage quota ($), and expected file transfer completion time (performance) simultaneously, and is motivated by the growing sensitivity of todays smartphone users to these attributes. Each time the individual attributes are calculated and updated, E$PA selects one of the three modes that minimizes the overall cost: (i) activation of both LTE and WiFi interfaces for parallel data transfer; (ii) LTE interface activation only; or (iii) WiFi interface activation only. The primary benefit of the E$PA is that it enables the smartphone to always operate in the “best” mode without the need for users manual control; the energy saving mode if the remaining battery energy is becoming nearly depleted; the cost-saving mode if the remaining data quota is almost running out; or, the maximum throughput mode if remaining data quota and battery life are both sufficient. Our multi-attribute cost model also takes into account the overheads (delays and energy consumption) associated with network interface turn-on/off and switching, as they impact the estimations of both performance (transfer time) and energy (battery life) attributes. Simulation results show that E$PA indeed chooses the “best” operational mode by maintaining dynamic balance among transfer time, energy consumption, and service charge.