The explosion of mobile applications and phenomenal adoption of mobile connectivity by end users make all-IP based 4G LTE as an ideal choice for providing Internet access on the go. LTE core network which handles device control-plane and data-plane traffic becomes susceptible to network resource constraints. To ease these constraints, Network Function Virtualization (NFV) provides high scalability and flexibility by enabling dynamic allocation of LTE core network resources. NFV achieves this by decomposing LTE Network Functions (NF) into multiple instances. However, LTE core network architecture which is designed considering fewer NF boxes does not fit well where decomposed NF instances add delays in network event execution. Certain control-plane events being time critical hurt data-plane traffic requirements defined by LTE standard. This paper proposes Fat-proxy which acts as a stand-alone execution engine of these critical network events. Through space uncoupling, we execute several signalling messages in parallel while skipping unnecessary messages to reduce event execution time and signalling overhead while ensuring highly available service access. We build our system prototype of open source LTE core network over virtualized platform. Our results show that we can reduce event execution time and signalling overhead upto 50% and 40%, respectively.

As powerline communication (PLC) technology does not require dedicated cabling and network setup, it can be used to easily connect multitude of IoT devices deployed in enterprise environments for sensing and control related applications. IEEE has standardized the PLC protocol in IEEE 1901, also known as HomePlug AV (HPAV) which has been widely adopted in mainstream PLC devices. A key weakness of HPAV protocol is that it does not support spectrum sharing. Currently, each link in an HPAV PLC network operates over the whole available spectrum, and only one link can operate at any time within a single collision domain. In this work, through an extensive measurement study of HPAV PLCs in a real enterprise environment using commodity off-the-shelf (COTS) HPAV PLC devices, we discover that spectrum sharing can significantly benefit enterprise level PLC networks. To this end, we propose a distributed spectrum sharing technique for enterprise HPAV PLC networks, and show that fine-grained distributed spectrum sharing on top of current HPAV MAC protocols can significantly boost the aggregated and per-link throughput, by allowing multiple PLC links to communicate concurrently, while requiring only a few modifications to the existing HPAV devices and protocols.

Mainstream video analytics uses a pre-trained DNN model with an assumption that inference input and training data follow the same probability distribution. However, this assumption does not always hold in the wild: autonomous vehicles may capture video with varying brightness; unstable wireless bandwidth calls for adaptive bitrate streaming of video; and, inference servers may serve inputs from heterogeneous IoT devices/cameras. In such situations, the level of input distortion changes rapidly, thus reshaping the probability distribution of the input. We present GearNN, an adaptive inference architecture that accommodates DNN inputs with varying distortions. GearNN employs an optimization algorithm to identify a tiny set of "distortion-sensitive" DNN parameters, given a memory budget. Based on the distortion level of the input, GearNN then adapts only the distortion-sensitive parameters, while reusing the rest of constant parameters across all input qualities. In our evaluation of DNN inference with dynamic input distortions, GearNN improves the accuracy (mIoU) by an average of 18.12% over a DNN trained with the undistorted dataset and 4.84% over stability training from Google, with only 1.8% extra memory overhead.

In this paper, we proposed frequency control in islanded microgrid using a coordinated control for multiple Battery Energy Storage System (BESS) considering state-of-charge (SOC) operating. BESSs are often proposed to support the frequency control in microgrid. Because BESSs can provide instantaneous power supply, it is very helpful for the power system to solve the power demand problem in real time. However, the most important things are operating within the proper SOC range. If the operation point is not allowed within the proper SOC range, reduce to BESS life and efficiency. if the power supply demand problem is solved alone through BESS, it will be outside the proper SOC range. So, we proposed the coordinated control diesel generator and BESSs considering SOC to maintain the supply-demand in microgrid. We using the Matlab/Simulink modeling and simulation analysis were performed.

IoT and deep learning based computer vision together create an immense market opportunity, but running deep neural networks (DNNs) on resource-constrained IoT devices remains challenging. Offloading DNN inference to an edge server is a promising solution, but limited wireless bandwidth bottlenecks its end-to-end performance and scalability. While IoT devices can adopt source compression to cope with the limited bandwidth, existing compression algorithms (or codecs) are not designed for DNN (but for human eyes), and thus, suffer from either low compression rates or high DNN inference errors. This paper presents GRACE, a DNN-aware compression algorithm that facilitates the edge inference by significantly saving the network bandwidth consumption without disturbing the inference performance. Given a target DNN, GRACE (i) analyzes DNN's perception model w.r.t both spatial frequencies and colors and (ii) generates an optimized compression strategy for the model -- one-time offline process. Next, GRACE deploys thus-generated compression strategy at IoT devices (or source) to perform online source compression within the existing codec framework, adding no extra overhead. We prototype GRACE on JPEG (the most popular image codec framework), and our evaluation results show that GRACE indeed achieves the superior compression performance over existing strategies for key DNN applications. For semantic segmentation tasks, GRACE reduces a source size by 23% compared to JPEG with similar interference accuracy (0.38% lower than GRACE). Further, GRACE even achieves 7.5% higher inference accuracy than JPEG with a commonly used quality level of 75 does. For classification tasks, GRACE reduces the bandwidth consumption by 90% over JPEG with the same inference accuracy.

LTE in unlicensed 5 GHz bands is being deployed by mobile operators for increased capacity. In this paper, we conduct an extensive measurement study with commodity LTE and Wi-Fi hardware to identify key coexistence challenges. Our study -- the first to include a commercial LAA base station -- confirms that LTE interference causes WiFi performance to degrade, harming 802.11ac high-throughput features. We then present DeMiLTE -- a system for commodity enterprise WiFi APs that detects, quantifies, and reacts to LTE interference. To our best knowledge, our solution is the first that achieves fair coexistence without modifying the LTE PHY/MAC, while still being fully-compliant to the 802.11ac standard. DeMiLTE's architecture is based on lightweight per-link interference detection and enables WiFi APs to mitigate LTE-induced performance degradation with minimal overhead. Our evaluation results show that DeMiLTE can provide up to 110% throughput gains and alleviate client disruption caused by LTE interference.

This paper presents VisIoT, a system that tracks the location of a wireless transmitter in IoT devices and displays it in the screen of an AR device such as smart glasses and tablet. The proposed system benefits existing IoT systems by enabling intuitive interaction between a user and IoT devices and further enhancing visualization of the data collected from IoT sensors. VisIoT achieves them through a combination of wireless sensing and camera motion tracking. By using the azimuth and elevation angles between the wireless transmitter and the camera-equipped mobile device, VisIoT can instantly identify the location of the IoT device from the camera image. This paper introduces novel azimuth and elevation estimation algorithms that leverage the phase difference of the signals from two antennas together with the tracked camera rotation. We prototype VisIoT using a tablet PC and a USRP software radio, and develop a software that tracks and visualizes the location of ZigBee nodes in real time. The evaluation results show that VisIoT can accurately track the nodes with the median position error of 6%.

In this positioning paper, we propose Chord, a holistic multi-user VR system for untethered mobile devices over 802.11ac/ax Wi-Fi. Taking a cross-layer approach, Chord brings numerous innovations to the application-layer design and VR-aware wireless network optimizations. We present our design and its preliminary evaluation on commodity smartphones and 802.11ac Wi-Fi, to demonstrate the feasibility of Chord.

Breakthroughs in deep learning, GPUs, and edge computing have paved the way for always-on, live video analytics. However, to achieve real-time performance, a GPU needs to be dedicated amongst a few video feeds. But, GPUs are expensive resources and a large-scale deployment requires supporting hundreds of video cameras - exorbitant cost prohibits widespread adoption. To ease this burden, we propose Tetris, a system comprising of several optimization techniques from computer vision and deep-learning literature blended in a synergistic manner. Tetris is designed to maximize the parallel processing of video feeds on a single GPU, with a marginal drop in inference accuracy. Tetris performs CPU-based tiling of active regions to combine activities across video feeds. resulting in a condensed input volume. It then runs the deep learning model on this condensed volume instead of individual feeds, which significantly improves the GPU utilization. Our evaluation on Duke MTMC dataset reveals that Tetris can process 4x video feeds in parallel compared to any of the existing methods used in isolation.

The 4G/5G cellular edge promises low-latency experiences anywhere, anytime. However, data charging gaps can arise between the cellular operators and edge application vendors, and cause over-/under-billing. We find that such gap can come from data loss, selfish charging, or both. It can be amplified in the edge, due to its low-latency requirements. We devise TLC, a Trusted, Loss-tolerant Charging scheme for the cellular edge. In its core, TLC enables loss-selfishness cancellation to bridge the gap, and constructs publicly verifiable, cryptographic proof-of-charging for mutual trust. We implement TLC with commodity edge nodes, OpenEPC and small cells. Our experiments in various edge scenarios validate TLC's viability of reducing the gap with marginal latency and other overhead.

As powerline communication (PLC) technology does not require dedicated cabling and network setup, it can be used to easily connect multitude of IoT devices deployed in enterprise environments for sensing and control related applications. IEEE has standardized the PLC protocol in IEEE 1901, also known as HomePlug AV (HPAV), which is widely adopted PLC standard. A key weakness of HPAV protocol is that it does not support any spectrum sharing strategies. Currently, each link in an HPAV PLC network operates over the whole available spectrum, and only one link can operate at any time within a single collision domain. We conducted a large scale measurement study using commodity HPAV PLC devices and analyzed channel characteristics of PLC networks in a real enterprise environment across space, time, and spectral dimensions. Based on our findings, we propose a distributed spectrum sharing technique for enterprise PLC networks, and show that fine-grained distributed spectrum sharing on top of current HPAV MAC protocols can boost the aggregated and per-link throughput by up to 60% and 250% respectively, by allowing multiple PLC links to communicate concurrently, while requiring a few modifications to the existing HPAV PLC devices and protocols.

An increasing number of depth sensors and surrounding-aware cameras are being installed in the new generation of cars. For example, Tesla Motors uses a forward radar, a front-facing camera, and multiple ultrasonic sensors to enable its Autopilot feature. Meanwhile, older or legacy cars are expected to be around in volumes, for at least the next 10 to 15 years. Legacy car drivers rely on traditional GPS for navigation services, whose accuracy varies 5 to 10 meters in a clear line-of-sight and degrades up to 30 meters in a downtown environment. At the same time, a sensor-rich car achieves better accuracy due to high-end sensing capabilities. To bridge this gap, we propose CoDrive, a system to provide a sensor-rich car's accuracy to a legacy car. We achieve this by correcting GPS errors of a legacy car on an opportunistic encounter with a sensor-rich car. CoDrive uses smartphone GPS of all participating cars, RGB-D sensors of sensor-rich cars, and road boundaries of a traffic scene to generate optimization constraints. Our algorithm collectively reduces GPS errors, resulting in accurate reconstruction of a traffic scene's aerial view. CoDrive does not require stationary landmarks or 3D maps. We empirically evaluate CoDrive which is shown to achieve a 90% and a 30% reduction in cumulative GPS error for legacy and sensor-rich cars respectively, while preserving the shape of the traffic.

Mobile video traffic is dominant in cellular and enterprise wireless networks. With the advent of myriads of applications from video telephony and streaming to virtual reality, network administrators face the challenge to provide high quality of experience (QoE) in the face of diverse wireless conditions and application contents. Yet, state-of-the-art networks lack analytics for QoE, as this requires support from the application or user feedback. While there are existing techniques to map quality of service (QoS) to QoE by training machine learning (ML) models without requiring user feedback, these techniques are limited to only few applications (e.g., Skype), due to insufficient QoE ground-truth annotation for ML. To address these limitations, we focus on video telephony applications and model key artefacts of spatial and temporal video QoE. Our key contribution is designing content- and device-independent metrics and training across diverse WiFi conditions. We show that our metrics achieve a median 90% accuracy by comparing with mean-opinion-score (MOS) from more than 200 users and 800 video samples. Our content-independent metrics significantly reduce the MOS prediction error of previous works and are validated over three popular video telephony applications - Skype, FaceTime and Google Hangouts.

Millimeter-wave (mmWave) technology is emerging as the most promising solution to meet the multi-fold demand increase for mobile data. Very short wavelength, high directionality, together with sensitivity to rampant blockages and mobility, however, render state-of-the-art mmWave technologies unsuitable for ubiquitous wireless coverage. In this work, we design and implement UbiG - a mmWave wireless access network - that can deliver ubiquitous gigabits per second wireless access consistently to the commercial-off-the-shelf IEEE 802.11ad devices. UbiG has two key design components: (1) a fast probing based beam alignment algorithm that can identify the best beam consistently with guaranteed latency in a mmWave link, and the algorithm scales well even with a very large number of beams; and (2) an infrastructure-side predictive ranking based fast access point switching algorithm to ensure seamless gigabits per second connectivity under mobility and blockage in a dense mmWave deployment. Our IEEE 802.11ad testbed experiments show that UbiG performs close to an "Oracle" solution that instantaneously knows the best beam and access point for gigabits per second data transmission to users.

Mobile advertisements influence customers' in-store purchases and boost in-store sales for brick-and-mortar retailers. Targeting mobile ads has become significantly important to compete with online shopping. The key to enabling targeted mobile advertisement and service is to learn shoppers' interest during their stay in the store. Precise shopper tracking and identification are essential to gain the insights. However, existing sensor-based or vision-based solutions are neither practical nor accurate; no commercial solutions today can be readily deployed in a large store. On the other hand, we recognize that most retail stores have the installation of surveillance cameras, and most shoppers carry Bluetooth-enabled smartphones. Thus, in this paper, we propose TAR to learn shoppers' in-store interest via accurate multi-camera people tracking and identification. TAR leverages widespread camera deployment and Bluetooth proximity information to accurately track and identify shoppers in the store. TAR is composed of four novel design components: (1) a deep neural network (DNN) based visual tracking, (2) a user trajectory estimation by using shopper visual and BLE proximity trace, (3) an identity matching and assignment to recognize shopper's identity, and (4) a cross-camera calibration algorithm. TAR carefully combines these components to track and identify shoppers in real-time. TAR achieves 90% accuracy in two different real-life deployments, which is 20% better than the state-of-the-art solution.

RF-based (mainly Wi-Fi and BLE) localization systems have been gaining the significant amount of attention from industry, since they do not require the deployment of any special and costly infrastructure such as cameras and depth sensors but leverage existing wireless Access Points (APs) and mobile clients. However, extensive field trials have shown that such solutions can be inaccurate (e.g., meter-level localization error) and/or impractical (e.g., requiring centrally-controlled, dense AP deployments). In this paper, we focus on millimeter-wave wireless networks, which are becoming increasingly popular for both indoors and outdoors connectivity, to overcome the aforementioned limitations and to provide accurate 3D localization. Specifically, we propose mWaveLoc, a system that exploits the small wavelength (millimeter) and directional communication of millimeter-wave (e.g., in 60 GHz) networks for accurate and practical 3D localization. mWaveLoc relies only on a single AP to track a device's position and works with existing off-the-shelf 802.11ad 60 GHz devices. Our implementation and experimental results on commodity 802.11ad testbeds show that mWaveLoc can achieve centimeter-level AP-client distance estimation accuracy and decimeter-level 3D localization accuracy in Line-Of-Sight settings.

We present a smartphone-based application, called LocBLE, for enabling users to estimate the location of nearby Bluetooth low energy (BLE) beacons. In contrast to existing BLE beacon-based proximity applications that can only show coarse-grained (immediate, near, and far) distance estimation, LocBLE's fine-grained estimation can enhance human-environment interactions.

LTE Network Function Virtualization (LTE-NFV) scales user services in a low cost fashion by transforming the centralized legacy LTE Core architecture to a distributed architecture. This distributed architecture makes multiple instances of LTE Network Functions (NFs) and virtualizes them on commodity data-center network. The functionality of LTE-NFV architecture breaks however, since the distributed NF instances connected via unreliable IP links delay the execution of critical events. The failure of time-critical events results in users' quality of service degradation and temporary service unavailability. In this paper, we propose a new way to virtualize LTE core network. We argue that logic-based NFs segregation should be done for NFV, instead of instance-based NFs segregation done in current NFV implementation. Our approach of ‘logic-based NFs segregation’ combines the logic of an event into a single NF, thus localizing the execution of critical events to one virtual machine. This way, only the localized entities exchange signalling messages, and the events do not experience large delays. We further reduce the delays by exploiting the parallelism in LTE network protocols; and partition these protocols such that their signalling messages run in parallel. In addition, we eliminate unnecessary messages to reduce the signalling overhead. We build our system prototype over OpenEPC LTE core network in virtualized platform. Our results show that we can reduce event execution time and signalling overhead up to 50% and 40%, respectively.

In today's always-connected world, we receive a large number of notifications on our mobile devices. These notifications cause interruptions, stress, and even impact users' lifestyle. To understand how users respond to notifications, we develop an application that monitors various features (e.g., importance) of the notifications, users' actions, and the level of users' engagement with the notifications. We recruit 30 users to use the application and monitor over 30 days, and subsequently find that 20% to 50% of the notifications generally get ignored by the users. In addition, we also solicit explicit feedback about the importance of notifications from 12 users over 14 days and identify the relation between perceived importance and users' engagement level. Based on this study, we identify the key characteristics of notifications and users' engagement, which is further substantiated by an onfine survey of 400+ users. In addition, we develop a notification manager that includes a machine learning based prediction model and that shows only the important notifications and delays the unimportant notifications. Our experimental results show that our notification manager automatically assesses the importance of notifications with more than 87% accuracy. We believe this work is a promising step toward intelligent personal assistant that manages notifications.

Despite years of innovative research and development, gigabit-speed 60 GHz wireless networks are still not mainstream. The main concern for network operators and vendors is the unfavorable propagation characteristics due to short wavelength and high directionality, which renders the 60 GHz links highly vulnerable to blockage and mobility. However, the advent of multi-band chipsets opens the possibility of leveraging the more robust WiFi technology to assist 60 GHz in order to provide seamless, Gbps connectivity. In this paper, we design and implement MUST, an IEEE 802.11-compliant system that provides seamless, high-speed connectivity over multi-band 60 GHz and WiFi devices. MUST has two key design components: (1) a WiFi-assisted 60 GHz link adaptation algorithm, which can instantaneously predict the best beam and PHY rate setting, with zero probing overhead; and (2) a proactive blockage detection and switching algorithm which can re-direct ongoing user traffic to the robust interface within sub-10 ms latency. Our experiments with off-the-shelf 802.11 hardware show that MUST can achieve 25-60% throughput gain over state-of-the-art solutions, while bringing almost 2 orders of magnitude cross-band switching latency improvement.

Despite years of innovative research and development, multi-Gbps 60 GHz wireless networks are still not mainstream. The unfavorable propagation characteristics due to short wavelength and high directionality, makes the 60 GHz links highly vulnerable to blockage and mobility. However, the advent of multi-band chipsets opens the possibility of leveraging the more robust WiFi technology to assist 60 GHz in order to provide seamless, Gbps connectivity. In this demonstration, we will present MUST, an 802.11-compliant real-time system that provides seamless, high-speed connectivity over multi-band 60 GHz and WiFi devices. MUST has two key design components: (1) a WiFi-assisted 60 GHz link adaptation algorithm, which can instantaneously predict the best beam and PHY rate setting, with zero probing overhead at 60 GHz; and (2) a proactive blockage detection and switching algorithm which can re-direct ongoing user traffic to the robust interface within sub-10 ms latency. We have implemented MUST on off-the-shelf devices where our experiments show high throughput gain and almost 2 orders of magnitude cross-band switching latency improvement over state-of-the-art solutions.

Major Wi-Fi Access Point (AP) vendors worldwide seek to provide gigabit wireless connectivity, by densely deploying MU-MIMO capable APs, which can support multiple, concurrent data streams to a group of clients, connected to them. However, MU-MIMO gains can only be achieved if an AP can identify groups of clients with homogenous configurations and orthogonal wireless channels, where concurrent transmissions will not cause inter-client interference. Hence, MU-MIMO performance is fundamentally depending on how the clients are assigned to APs. Our experiments with 802.11ac commodity testbeds show that state-of-the-art client assignment algorithms are MU-MIMO oblivious and limit the MU-MIMO grouping opportunities in realistic settings. In this paper, we design and implement MAPS, an MU-MIMO-Aware AP Selection algorithm that is 802.11-compliant and can boost network's MU-MIMO throughput gains. We verified MAPS' gains over legacy designs via extensive experiments with 802.11ac commodity testbeds.

Many mobile apps, including augmented-reality games, bar-code readers, and document scanners, digitize information from the physical world by applying computer-vision algorithms to live camera data. However, because camera permissions for existing mobile operating systems are coarse (i.e., an app may access a camera's entire view or none of it), users are vulnerable to visual privacy leaks. An app violates visual privacy if it extracts information from camera data in unexpected ways. For example, a user might be surprised to find that an augmented-reality makeup app extracts text from the camera's view in addition to detecting faces. This paper presents results from the first large-scale study of visual privacy leaks in the wild. We build CamForensics to identify the kind of information that apps extract from camera data. Our extensive user surveys determine what kind of information users expected an app to extract. Finally, our results show that camera apps frequently defy users' expectations based on their descriptions.

Enterprise wireless networks face significant challenges to deliver Quality-of-Experience (QoE) with the variety of mobile applications. One of the fundamental challenges is that the traditional definition of network capacity (often defined as throughput capacity) is not sufficient to reflect applications' requirements in wireless networks. In this paper, we propose to rethink the network capacity of wireless networks to better incorporate QoE. Specifically, we first propose a novel concept of an Experiential Capacity Region (ExCR) for wireless networks. ExCR is defined as a set of simultaneous application flows whose QoE requirements can be satisfied by the network. Next, we present the infrastructure based ExBox system that measures per-application QoE metrics and determines the ExCR for wireless networks to better serve a set of mobile application flows. In its core, ExBox employs light-weight machine learning techniques that are tailored for dynamic wireless environments. Through both large-scale simulations and extensive real-life experiments on WiFi and LTE networks, we show that ExBox delivers QoE in admission control decision with a precision of ≈ 0.8 - 0.9, even when clients experience diverse channel quality. Moreover, ExBox quickly adapts to changing network environments without much overhead.

Powerline communication (PLC) provides inexpensive, secure and high speed network connectivity, by leveraging the existing power distribution networks inside the buildings. While PLC technology has the potential to improve connectivity and is considered a key enabler for sensing, control, and automation applications in enterprises, it has been mainly deployed for improving connectivity in homes. Deploying PLCs in enterprises is more challenging since the power distribution network is more complex as compared to homes. Moreover, existing PLC technologies such as HomePlug AV have not been designed for and evaluated in enterprise deployments. To this end, we give guidlines for designing PLC networks for providing ubiquitous connectivity in enterprises, based on measurement study of PLC performance in enterprise settings using commodity HomePlug AV PLC devices. Based on our findings, we propose that careful planning of PLC network topology, routing and spectrum sharing can significantly boost performance of enterprise PLC networks.

Multi-User MIMO, the hallmark of IEEE 802.11ac and the upcoming 802.11ax, promises significant throughput gains by supporting multiple concurrent data streams to a group of users. However, identifying the best-throughput MU-MIMO groups in commodity 802.11ac networks poses three major challenges: a) Commodity 802.11ac users do not provide full CSI feedback, which has been widely used for MU-MIMO grouping. b) Heterogeneous channel bandwidth users limit grouping opportunities. c) Limited-resource on APs cannot support computationally and memory expensive operations, required by existing algorithms. Hence, state-of-the-art designs are either not portable in 802.11ac APs, or perform poorly, as shown by our testbed experiments. In this paper, we design and implement MUSE, a lightweight user grouping algorithm, which addresses the above challenges. Our experiments with commodity 802.11ac testbeds show MUSE can achieve high throughput gains over existing designs.

A novel attack model is proposed against the existing wireless link-based source identification, which classifies packet sources according to the physical-layer link signatures. A link signature is believed to be a more reliable indicator than an IP or MAC address for identifying packet source, as it is generally harder to modify/forge. It is therefore expected to be a future authentication against impersonation and DoS attacks. However, if an attacker is equipped with the same capability/hardware as the authenticator to process physical-layer signals, a link signature can be easily manipulated by any nearby wireless device during the training phase. Based on this finding, we propose an attack model, called the analog man-in-the-middle (AMITM) attack, which utilizes the latest full-duplex relay technology to inject semi-controlled link signatures into authorized packets and reproduce the injected signature in the fabricated packets. Our experimental evaluation shows that with a proper parameter setting, 90% of fabricated packets are classified as those sent from an authorized transmitter. A countermeasure against this new attack is also proposed for the authenticator to inject link-signature noise by the same attack methodology.

Context-aware services nowadays offer incentive to user-reported context information , which inevitably solicits malicious users to cheat by submitting fabricated context claims. Conventional countermeasures based on Trusted Computing Base typically focus on particular context of interest, while disregarding the availability of various types of context information and the intrinsic correlation among them. In this work we propose a context claim verification scheme that interrogates correlated contexts of multiple dimensions to corroborate or contradict the reported context. Specifically, it first learns and models the context correlation with a Bayesian Multinet. Given a claim consisting of reported context and witnessing evidence, the scheme performs Bayesian inference with the evidence to verify the reported context. The verification process is light-weight, and can be applied to arbitrary types of context with a single model learnt. Evaluations on Reality Mining dataset and synthetic dataset validates choice of Multinet for data modeling, and demonstrate the feasibility of our scheme in context verification.

Abstract With the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

With the great success of LTE(-A) outdoor, LTE-based small cell technology has become popular and is penetrating indoor enterprise environment, co-existing with WiFi networks, to provide better user experience or Quality-of-Experience (QoE). However, accurate estimation of LTE links is challenging and critical to continue providing QoE for many enterprise applications (e.g., video/audio) and services (network selection). While prior work on LTE link throughput estimation depends mostly on a single factor (e.g., link rate), we argue that it needs to consider more factors to improve the estimation to meet increasing demands on QoE. In this paper, we propose a new metric, called Pulsar (Per-user LTE ShAre of Resources), that estimates per flow throughput in LTE networks by leveraging both underlying channel information and application traffic characteristics. Our extensive evaluation study through ns-3 shows that Pulsar reduces the estimation error more than 92%, compared to prior work, in various scenarios, while keeping estimation overhead low.

One key requirement for storage clouds is to be able to retrieve data quickly. Recent system measurements have shown that the data retrieving delay in storage clouds is highly variable, which may result in a long latency tail. One crucial idea to improve the delay performance is to retrieve multiple data copies by using parallel downloading threads. However, how to optimally schedule these downloading threads to minimize the data retrieving delay remains to be an important open problem. In this paper, we develop low-complexity thread scheduling policies for several important classes of data downloading time distributions, and prove that these policies are either delay-optimal or within a constant gap from the optimum delay performance. These theoretical results hold for an arbitrary arrival process of read requests that may contain finite or infinite read requests, and for heterogeneous MDS storage codes that can support diverse storage redundancy and reliability requirements for different data files. Our numerical results show that the delay performance of the proposed policies is significantly better than that of First-Come-First-Served (FCFS) policies considered in prior work.

This paper demonstrates that it is possible to leverage WiFi signals from commodity mobile devices to enable hands-free drawing in the air. While prior solutions require the user to hold a wireless transmitter, or require custom wireless hardware, or can only determine a pre-defined set of hand gestures, this paper introduces WiDraw, the first hand motion tracking system using commodity WiFi cards, and without any user wearables. WiDraw harnesses the Angle-of-Arrival values of incoming wireless signals at the mobile device to track the user's hand trajectory. We utilize the intuition that whenever the user's hand occludes a signal coming from a certain direction, the signal strength of the angle representing the same direction will experience a drop. Our software prototype using commodity wireless cards can track the user's hand with a median error lower than 5 cm. We use WiDraw to implement an in-air handwriting application that allows the user to draw letters, words, and sentences, and achieves a mean word recognition accuracy of 91%.

This paper presents TypingRing, a wearable ring platform that enables text input into computers of different forms, such as PCs, smartphones, tablets, or even wearables with tiny screens. The basic idea of TypingRing is to have a user wear a ring on his middle finger and let him type on a surface - such as a table, a wall, or his lap. The user types as if a standard QWERTY keyboard is lying underneath his hand but is invisible to him. By using the embedded sensors TypingRing determines what key is pressed by the user. Further, the platform provides visual feedback to the user and communicates with the computing device wirelessly. This paper describes the hardware and software prototype of TypingRing and provides an in-depth evaluation of the platform. Our evaluation shows that TypingRing is capable of detecting and sending key events in real-time with an average accuracy of 98.67%. In a field study, we let seven users type a paragraph with the ring, and we find that TypingRing yields a reasonable typing speed (e.g., 33-50 keys per minute) and their typing speed improves over time.

WiFi based indoor positioning has recently gained more attention due to the advent of the IEEE 802.11v standard, requirements by the FCC for E911 calls, and increased interest in location-based services. While there exist several indoor localization techniques, we find that these techniques tradeoff either accuracy, scalability, pervasiveness or cost -- all of which are important requirements for a truly deployable positioning solution. Wireless signal-strength based approaches suffer from location errors, whereas time-of-flight (ToF) based solutions provide good accuracy but are not scalable. Recent solutions address these issues by augmenting WiFi with either smartphone sensing or mobile crowdsourcing. However, they require tight coupling between WiFi infrastructure and a client device, or they can determine the client's location only if it is mobile. In this paper, we present CUPID2.0 which improved our previously proposed CUPID indoor positioning system to overcome these limitations. We achieve this by addressing the fundamental limitations in Time-of-Flight based localization and combining ToF with signal strength to address scalability. Experiments from $6$ cities using $40$ different mobile devices, comprising of more than $2.5$ million location fixes demonstrate feasibility. CUPID2.0 is currently under production, and we expect CUPID2.0 to ignite the wide adoption of WLAN-based positioning systems and their services.

Many opportunistic scheduling techniques are impractical because they require accurate channel state information (CSI) at the transmitter. In this paper, we investigate the scheduling of unicast and multicast services in a downlink network with a very limited amount of feedback information. Specifically, unicast users send imperfect (or no) CSI and infrequent acknowledgements (ACKs) to a base station, and multicast users only report infrequent ACKs to avoid feedback implosion. We consider the use of physical-layer rateless codes, which not only combats channel uncertainty, but also reduces the overhead of ACK feedback. A joint scheduling and power allocation scheme is developed to realize multiuser diversity gain for unicast service and multicast gain for multicast service. We prove that our scheme achieves a near-optimal throughput region. Our simulation results show that our scheme significantly improves the network throughput over schemes employing fixed-rate codes or using only unicast communications.

Indoor navigation is typically achieved by an operational localization system among a range of location-based services it provides. However, the construction of a localization map, which is prerequisite for binding sensed observation in sensing space to individual locations in geographic space, remains a challenging task to date. In this work, we propose an indoor navigation system that alleviates the need for constructing a localization map and instead provides navigation in signal space. The main idea behind our approach is to construct a sensing map consisting of signal observations (WiFi Clusters) and connecting dead-reckoning segments obtained through mobile sensing capabilities (traces of accelerometer and digital compass reading). To this end, we design a prototype to demonstrate effective construction of such sensing map with energy-efficient sensors and crowd-sourcing, and its ability to support accurate navigation.

This paper presents SAIL, a Single Access Point Based Indoor Localization system. Although there have been advances in WiFi-based positioning techniques, we find that existing solutions either require a dense deployment of access points (APs), manual fingerprinting, energy hungry WiFi scanning, or sophisticated AP hardware. We design SAIL using a single commodity WiFi AP to avoid these restrictions. SAIL computes the distance between the client and an AP using the propagation delay of the signal traversing between the two, combines the distance with smartphone dead-reckoning techniques, and employs geometric methods to ultimately yield the client's location using a single AP. SAIL combines physical layer (PHY) information and human motion to compute the propagation delay of the direct path by itself, eliminating the adverse effect of multipath and yielding sub-meter distance estimation accuracy. Furthermore, SAIL systematically addresses some of the common challenges towards dead-reckoning using smartphone sensors and achieves 2-5x accuracy improvements over existing techniques. We have implemented SAIL on commodity wireless APs and smartphones. Evaluation in a large-scale enterprise environment with 10 mobile users demonstrates that SAIL can capture the user's location with a mean error of 2.3m using just a single AP.

Wireless performance depends directly on the quality of the channel. A wireless transmitter can improve its performance by estimating and transmitting on only the strongest channel, which can be of significantly higher quality than a weak channel (yielding up to 100% rate improvement). It is considered impossible to predict the quality of the unseen channels. Thus, the only way to identify the strongest channel is by probing each channel individually, incurring large over- heads. The key contribution of this paper is a discovery of previously unobserved properties of the wireless channel that makes it possible to predict the the strongest of a set of channels from the measurements collected only on a single channel. We confirm the properties through measurements and present a theoretical analysis that explains their nature. Our proposed system, CSpy, utilizes these observations to predict the strongest channel. CSpy is the first to reliably estimate the strongest channel by utilizing channel responses extracted from off-the-shelf wireless chipsets, without probing any additional channels. By tracking the strongest channel, CSpy improves performance by up to 100% in comparison to channel agnostic schemes.

Despite of several years of innovative research, indoor localization is still not mainstream. Existing techniques either employ cumbersome fingerprinting, or rely upon the deployment of additional infrastructure. Towards a solution that is easier to adopt, we propose CUPID, which is free from these restrictions, yet is comparable in accuracy. While existing WiFi based solutions are highly susceptible to indoor multipath, CUPID utilizes physical layer (PHY) information to extract the signal strength and the angle of only the direct path, successfully avoiding the effect of multipath reflections. Our main observation is that natural human mobility, when combined with PHY layer information, can help in accurately estimating the angle and distance of a mobile device from an wireless access point (AP). Real-world indoor experiments using off-the-shelf wireless chipsets confirm the feasibility of CUPID. In addition, while previous approaches rely on multiple APs, CUPID is able to localize a device when only a single AP is present. When a few more APs are available, CUPID can improve the median localization error to 2.7m, which is comparable to schemes that rely on expensive fingerprinting or additional infrastructure.

Many popular mobile applications require the continuous monitoring and sharing of a mobile user's location. However, exploiting a user's location leads to disclosing sensitive information about the users daily activity. Several location privacy-preserving schemes have been proposed, but it remains challenging for a user to achieve visibility of the associated threats as well as to control the impact of those threats. This paper presents an adaptive location privacy-preserving system (ALPS) that allows for a user to control the level of privacy disclosure with different quality of location-based service (LBS). We have identified key attack models on location tracking using powerful map-matching algorithms, and then defined a scheme that allows a user to control the privacy of tracking information. We have implemented ALPS on Android OS and evaluated the implementation extensively via trace-based simulation, showing the effectiveness of user-controllable privacy preservation.

Multihop wireless relays can extend the area of network connectivity instantly and efficiently. However, due to the spatial dependence of wireless link-quality, the deployment of relay nodes requires extensive, expensive measurement, and management efforts. This paper presents a mobile autonomous router system, (MARS) through which a relay router autonomously seeks and adjusts the best "receptionâ position for itself and cooperatively forms a string-type relay network with other neighboring routers. Specifically, MARS 1) accurately characterizes spatial link-quality through a new measurement technique, 2) effectively probes/optimizes node positioning via a spatial probing algorithm, and 3) maintains error-tolerant position information via an inexpensive positioning algorithm. MARS has been prototyped with both a commodity mobile robot and a wireless router with IEEE 802.11 cards. Our experimental evaluation of both the MARS prototype and ns-2-based simulation show that MARS achieves an average of 95 percent accuracy in link-quality measurements, and reduces the measurement effort necessary for the optimization of a node's location by two-thirds, compared to exhaustive spatial probing.

Network deduplication (dedup) is an attractive approach to improve network performance for mobile devices. With traditional deduplication, the dedup~source uses only the portion of the cache at the dedup~destination that it is aware of. We argue in this work that in a mobile environment, the dedup~destination (say the mobile) could have accumulated a much larger cache than what the current dedup~source is aware of. This can occur because of several reasons ranging from the mobile consuming content through heterogeneous wireless technologies, to the mobile moving across different wireless networks.

Crowd-sourced Wi-Fi-based localization systems utilize user input for RF scene analysis and map construction. Such systems reduce the deployment cost and privacy concerns that expert-based site survey systems can create. However, the main bottleneck of such crowd-sourcing localization systems is a bootstrapping stage, where lack of contributions by users results in no accuracy guarantee and frequent unnecessary prompting for users' input, even for explored areas. In this paper, we propose a crowd-sourcing localization system that uses both Wi-Fi scene analysis and Bluetooth beacons to address the insufficient contribution challenge. After prompting for user input, the mobile device not only submits Wi-Fi fingerprint to a map server, but also enables Bluetooth beacons to disseminate/share its location and fingerprint information to quickly populate the signal map. Then, subsequent user devices entering the area can discover the Bluetooth beacons and are able to instantly obtain room-level location information without causing unnecessary prompting to users. We implement our proposed system in the Linux OS and evaluate the prototype extensively through both experiments and simulation. Our evaluation results show that using Bluetooth beacons help to improve signal map growth, while maintaining reasonable localization accuracy.

Mobile data usage over cellular networks has been dramatically increasing over the past years. Wi-Fi based wireless networks offer a high-bandwidth alternative for offloading such data traffic. However, intermittent connectivity, and battery power drain in mobile devices, inhibits always-on connectivity even in areas with good Wi-Fi coverage. This paper presents WiFisense, a system that employs user mobility information retrieved from low-power sensors (e.g., accelerometer) in smartphones, and further includes adaptive Wi-Fi sensing algorithms, to conserve battery power while improving Wi-Fi usage. We implement the proposed system in Android-based smartphones and evaluate the implementation in both indoor and outdoor Wi-Fi networks. Our evaluation results show that WiFisense saves energy consumption for scans by up to 79% and achieves considerable increase in Wi-Fi usage for various scenarios.

Cognitive radios (CRs) can mitigate the impending spectrum scarcity problem by utilizing their capability of accessing licensed spectrum bands opportunistically. While most existing work focuses on enabling such opportunistic spectrum access for stationary CRs, mobility is an important concern to secondary users (SUs) because future mobile devices are expected to incorporate CR functionality. In this paper, we identify and address three fundamental challenges encountered specifically by mobile SUs. First, we model channel availability experienced by a mobile SU as a two-state continuous-time Markov chain (CTMC) and verify its accuracy via in-depth simulation. Then, to protect primary/incumbent communications from SU interference, we introduce guard distance in the space domain and derive the optimal guard distance that maximizes the spatio-temporal spectrum opportunities available to mobile CRs. To facilitate efficient spectrum sharing, we formulate the problem of maximizing secondary network throughput within a convex optimization framework, and derive an optimal, distributed channel selection strategy. Our simulation results show that the proposed spectrum sensing and distributed channel access schemes improve network throughput and fairness significantly, and reduce SU energy consumption for spectrum sensing by up to 74%.

Despite the successful use of local image features for large-scale object recognition, they are not effective in recognizing book spines on bookshelves. This is because some book spines contain only text components that do not yield distinguishing image features. To overcome this issue, we develop a new approach that combines a text-based spine recognition pipeline with an image feature-based spine recognition pipeline. The text within the book spine image is recognized and used as keywords to search a book spine text database. The image features of the book spine image are searched through a book spine image database. The search results of the two approaches are then carefully combined to form the final result. We implement the proposed hybrid book recognition pipeline used in a book inventory management system, and conduct extensive experiments to evaluate its performance. The experimental results show that while text-based or image feature-based systems only achieve a recall of 72%, the proposed hybrid system achieves a recall of ~91%.

Many emerging location-aware applications require position information. However, these applications rarely use celltower-based localization because of its inaccuracy, preferring instead to use the more energy-hungry GPS.In this paper, we present CAPS, a Cell-ID Aided Positioning System. CAPS leverages near-continuous mobility and the position history of a user to achieve significantly better accuracy than the celltower-based approach, while keeping energy overhead low. CAPS is designed based on the insight that users exhibit consistency in routes traveled, and that cell-ID transition points that the user experiences can, on a frequently traveled route, uniquely identify position. To this end, CAPS uses a cell-ID sequence matching technique to estimate current position based on the history of cell-ID and GPS position sequences that match the current cell-ID sequence. We have implemented CAPS on Android-based smartphones and have extensively evaluated it at different locations, and for different platforms and carriers. Our evaluation results show that CAPS can save more than 90% of the energy spent by the positioning system compared to the case where GPS is always used, while providing reasonably accurate position information with errors less than 20% of the celltower-based scheme.

During their lifetime, multihop wireless mesh networks (WMNs) experience frequent link failures caused by channel interference, dynamic obstacles, and/or applications' bandwidth demands. These failures cause severe performance degradation in WMNs or require expensive manual network management for their real-time recovery. This paper presents an autonomous network reconfiguration system (ARS) that enables a multiradio WMN to autonomously recover from local link failures to preserve network performance. By using channel and radio diversities in WMNs, ARS generates necessary changes in local radio and channel assignments in order to recover from failures. Next, based on the thus-generated configuration changes, the system cooperatively reconfigures network settings among local mesh routers. ARS has been implemented and evaluated extensively on our IEEE 802.11-based WMN test-bed as well as through ns2-based simulation. Our evaluation results show that ARS outperforms existing failure-recovery schemes in improving channel-efficiency by more than 90% and in the ability of meeting the applications' bandwidth demands by an average of 200%.

Manual generation of a book inventory is time-consuming and tedious, while deployment of barcode and radio-frequency identification (RFID) management systems is costly and affordable only to large institutions. In this paper, we design and implement a mobile book recognition system for conveniently generating an inventory of books by snapping photos of a bookshelf with a smartphone. Since smartphones are becoming ubiquitous and affordable, our inventory management solution is cost-effective and very easy to deploy. Automatic and robust book recognition is achieved in our system using a combination of spine segmentation and bag-of-features image matching. At the same time, the location of each book is inferred from the smartphone's sensor readings, including accelerometer traces, digital compass measurements, and WiFi signatures. This location information is combined with the image recognition results to construct a location-aware book inventory. We demonstrate the effectiveness of our book spine recognition and location estimation techniques in recognition experiments and in an actual mobile book recognition system.

Information on site-specific spectrum characteristics is essential to evaluate and improve the performance of wireless networks. However, it is usually very costly to obtain accurate spectrum-condition information in heterogeneous wireless environments. This paper presents a novel spectrum-survey system, called Sybot (Spectrum survey robot), that guides network engineers to efficiently monitor the spectrum condition (e.g., RSS) of WiFi networks. Sybot effectively controls mobility and employs three disparate monitoring techniques - complete, selective, and diagnostic - that help produce and maintain an accurate spectrum-condition map for challenging indoor WiFi networks. By adaptively triggering the most suitable of the three techniques, Sybot captures spatio-temporal changes in spectrum condition. Moreover, based on the monitoring results, Sybot automatically determines several key survey parameters, such as site-specific measurement time and space granularities. Sybot has been prototyped with a commodity IEEE 802.11 router and Linux OS, and experimentally evaluated, demonstrating its ability to generate accurate spectrum-condition maps while reducing the measurement effort (space, time) by more than 56%.

Location-based applications have become increasingly popular on smartphones over the past years. The active use of these applications can however cause device battery drain owing to their power-intensive location-sensing operations. This paper presents an adaptive location sensing framework that significantly improves the energy efficiency of smartphones running location-based applications. The underlying design principles of the proposed framework involve substitution, suppression, piggybacking, and adaptation of applications' location-sensing requests to conserve energy. We implement these design principles on Android-based smartphones as a middleware. Our evaluation results show that the design principles reduce the usage of the power-intensive GPS (Global Positioning System) by up to 98% and improve battery life by up to 75%.

We present novel Localized sElf-reconfiGuration algOrithms (LEGO) for a multi-radio wireless mesh network to autonomously and effectively recover from wireless link failures. First, LEGO locally detects link failures by accurately monitoring the network condition and, upon detection of a failure, triggers network reconfiguration. Second, it dynamically forms/deforms a local group for cooperative network reconfiguration among local mesh routers in a fully-distributed manner. Next, LEGO intelligently generates a local network reconfiguration plan through which a thus-formed group recovers from local failures, while keeping network changes to minimum. Finally, by figuring local channel utilization and reconfiguration cost in its planning, LEGO maximizes the network's ability to meet diverse links' QoS demands. LEGO has been implemented on a Linux-based system and experimented on a reallife testbed, demonstrating its effectiveness in recovering from link failures and its improvement of channel efficiency by up to 92%.

Multi-homed mobile hosts in physical proximity may spontaneously team up to form a community and run high-bandwidth applications by pooling their low wireless widearea network (WWAN) bandwidths together for communication with a remote application server. Utilizing their high-bandwidth wireless local-area network (WLAN), the thus-teamed mobile hosts can aggregate and distribute the application content among themselves. This paper first justifies the need for such a mobile collaborative community (MC2), or a community, to improve user-perceived network bandwidth and utilization. Then, existing one-to-one communication protocols like TCP are shown to suffer significant performance degradation due to frequent out-of-order packet deliveries. To address this TCP problem, we propose a proxy-based inverse multiplexer, called PRISM, that enables TCP to efficiently utilize the community members' WWAN connections while avoiding the performance degradation. PRISM runs at the proxy's network layer as a routing component and stripes each TCP flow over multiple WWAN links by exploiting the transport-layer feedback information. Moreover, it masks a variety of adverse effects specific to each WWAN link via an intelligent ACK-control mechanism. Finally, PRISM enables TCP to respond correctly to dynamically-changing network states through a sender-side enhancement of congestion control. PRISM has been evaluated with experimentation on a testbed as well ns-2-based simulation. Our experimental evaluation has shown PRISM to improve TCP's performance by up to 310% even with two collaborative mobile hosts. Our in-depth simulation study has also shown that PRISM delivers a near-optimal aggregated bandwidth in the community, and improves network utilization significantly

This paper presents a highly efficient and accurate link-quality measurement framework, called EAR (Efficient and Accurate link-quality monitoR), for multi-hop wireless mesh networks, that has several salient features. First, it exploits three complementary measurement schemes: passive, cooperative, and active monitoring. EAR maximizes the measurement accuracy by (i) dynamically and adaptively adopting one of these schemes and (ii) opportunistically exploiting the nicast application traffic present in the network, while minimizing the measurement overhead. Second, EAR effectively identifies the existence of wireless link asymmetry by measuring the quality of each link in both directions of the link, thus improving the utilization of network capacity by up to 114%. Finally, its reliance on both the network layer and the IEEE 802.11-based device driver solutions makes EAR easily deployable in existing multi-hop wireless mesh networks without system recompilation or MAC firmware modification. EAR has been evaluated extensively via both ns-2-based simulation and experimentation on our Linux-based implementation. Both simulation and experimentation results have shown EAR to provide highly accurate link-quality measurements with minimum overhead.

Multi-homed mobile hosts situated in physical proximity may spontaneously team up to run high-bandwidth applications by pooling their low wireless wide-area network (WWAN) bandwidths together for communication with a remote application server and utilizing their high-bandwidth wireless local-area network (WLAN) in ad-hoc mode for aggregation and distribution of application contents among the participating mobile hosts. In this paper, we first describe the need for such a mobile collaborative community, or a community, in which multi-homed mobile hosts exploit the diversity of WWAN connections to improve a user-perceived bandwidth and network utilization. Then, we show that existing one-to-one communication protocols like TCP suffer significant performance degradation due to frequent packet reordering and heterogeneity of WWAN links in the community.To address the above TCP problem, we propose a proxy-based inverse multiplexer, called PRISM, that enables TCP to efficiently utilize the community members' WWAN connections. PRISM runs at a proxy's network layer as a routing component and stripes each TCP flow over multiple WWAN links by exploiting the transport-layer feedback information. Moreover, it masks variety of adverse effects specific to each WWAN link via intelligent ACK-control mechanism. Finally, PRISM includes a sender-side enhancement of congestion control, enabling TCP to respond correctly to dynamically-changing network states.We have evaluated the PRISM protocol using both experimentation and ns-2-based simulation. Our experimental evaluation has shown PRISM to improve TCP's performance by up to 310% even with two collaborative mobile hosts. Our in-depth simulation study also shows that PRISM delivers a near-optimal aggregated bandwidth in the community formed by heterogeneous mobile hosts, and improves network utilization significantly.

In this paper we consider TCP based applications that use bandwidth guarantees, but can also benefit from any additional best-effort service offered by the network. We show that default TCP cannot offer such applications the aggregate throughput offered by the two services. We propose an adaptation of TCP called GTCP that uses changes to TCP's congestion control mechanisms to provide the optimal aggregate throughput. GTCP does not require any additional implementation overheads, and does not change the TCP receiver. Through simulations we show that GTCP achieves significantly better performance than default TCP in the target environment.

Numerous transport protocols have been proposed in related work for use by mobile hosts over wireless environments. A common theme among the design of such protocols is that they specifically address the distinct characteristics of the last-hop wireless link, such as random wireless errors, round-trip time variations, blackouts, handoffs, etc. In this paper, we argue that due to the defining role played by the wireless link on a connection's performance, locating the intelligence of a transport protocol at the mobile host that is adjacent to the wireless link can result in distinct performance advantages. To this end, we present a receiver-centric transport protocol called RCP (Reception Control Protocol) that is a TCP clone in its general behavior, but allows for better congestion control, loss recovery, and power management mechanisms compared to sender-centric approaches. More importantly, in the context of recent trends where mobile hosts are increasingly being equipped with multiple interfaces providing access to heterogeneous wireless networks, we show that a receiver-centric protocol such as RCP can enable a powerful and comprehensive transport layer solution for such multi-homed hosts. Specifically, we describe how RCP can be used to provide: (i) a scalable solution to support interface specific congestion control for a single active connection; (ii) seamless server migration capability during handoffs; and (iii) effective bandwidth aggregation when receiving data through multiple interfaces, either from one server, or from multiple replicated servers. We use both packet level simulations, and real Internet experiments to evaluate the proposed protocol.