In this paper, we propose a novel communication framework, Acoustic-WiFi, that utilizes the acoustic channel (i.e., microphone/speaker) as a control channel to develop more efficient Wi-Fi networks. We used the Acoustic-WiFi framework to develop a Power Saving Mode (PSM) scheme;A2PSM, and a smart contention resolution scheme;Harmony. InA2PSM, we propose an acoustic channel assisted power saving scheme for the Wi-Fi interface, which addresses the inefficiency of the existing power saving schemes in smart devices. In this scheme, we leverage the low power consumption of the acoustic interfaces to reduce the wakeup events of the Wi-Fi interface when it is in power saving mode. We develop a small-scale prototype testbed on real smartphones to evaluate the proposedA2PSMscheme. Experiments show thatA2PSMcould save up to more than 25% more power than the existing schemes. InHarmony, we address the overhead of the Wi-Fi backoff scheme (i.e., contention window countdown, DIFS) and reduce the overall collisions among the devices.Harmonyuses the acoustic channel for contention resolution in Wi-Fi networks. We also evaluateHarmonyusing real testbed and simulation. Experiments using the testbed show a 40% gain in throughput over the 802.11 Wi-Fi backoff scheme, while simulation results show a gain of more than 27% for dense networks.

Having an efficient onboarding process is a pivotal step to utilize and provision the IoT devices for accessing the network infrastructure. However, the current process to onboard IoT devices is time-consuming and labor-intensive, which makes the process vulnerable to human errors and security risks. In order to have a streamlined onboarding process, we need a mechanism to reliably associate each digital identity with each physical device. We design an onboarding mechanism called MAIDE to fill this technical gap. MAIDE is an Augmented Reality (AR)-facilitated app that systematically selects multiple measurement locations, calculates measurement time for each location and guides the user through the measurement process. The app also uses an optimized voting-based algorithm to derive the device-to-ID mapping based on measurement data. This method does not require any modification to existing IoT devices or the infrastructure and can be applied to all major wireless protocols such as BLE, and WiFi. Our extensive experiments show that MAIDE achieves high device-to-ID mapping accuracy. For example, to distinguish two devices on a ceiling in a typical enterprise environment, MAIDE achieves ~95% accuracy by measuring 5 seconds of Received Signal Strength (RSS) data for each measurement location when the devices are 4 feet apart.

Mobile Augmented Reality (MAR) is going to play an important role in industrial automation. In order to tag a physical object in the MAR world, a smart phone running MAR-based applications must know the precise location of an object in the real world. Tracking and localizing a large number of objects in an industrial environment can become a huge burden for the smart phone due to compute and battery requirements. In this paper we propose GLAMAR, a novel framework that leverages externally provided geo-location of the objects and IMU sensor information (both of which can be noisy) from the objects to 10-cate them precisely in the MAR world. GLAMAR offloads heavy-duty computation to the edge and supports building MAR-based applications using commercial development packages. We develop a regenerative particle filter and a continuously improving transformation matrix computation methodology to dramatically improve the positional accuracy of objects in the real and the AR world. Our prototype implementation on Android platform using ARCore shows the practicality of GLAMAR in developing MAR-based applications with high precision, efficiency, and more realistic experience. GLAMAR is able to achieve less then 10cm error compared to the ground truth for both stationary and moving objects and reduces the CPU overhead by 83% and battery consumption by 80% for mobile devices.

In order to create services using IoT devices, the underlying network infrastructure must support large number of such devices with different underlying protocols, and diverse requirements from the service applications (privacy, reliability and QoS guarantee, etc.). Many of these requirements can be realized by implementing an in-network packet forwarding policy in the infrastructure supporting direct device-to-device communications. However, with large number of devices deployed in the IoT network, the number of rules required for policy enforcement grows very rapidly, and it becomes an infrastructural challenge to installing and managing the rules in switches/routers. We argue that attaching service and role-based labels to address IoT devices can significantly reduce the number of rules by using wild-cards. We formulate a scheme that can produce the optimum length labels for representing the service attributes of the communicating IoT devices. Due to non-convex nature of the optimization, we develop two heuristic solutions for the label generating scheme. Through evaluation using a simulated but practical IoT network environment with large number of devices, we demonstrate the benefits of the scheme that can reduce the number of rules by several orders of multitude.

With the current significant penetration of mobile devices (i.e. smartphones and tablets) and the tremendous increase in the number of the corresponding mobile applications, they have become an indispensable part of our lives. Nowadays, there is a significant growth in the number of sensitive applications such as personal health applications, personal financial applications, home monitoring applications, etc. In addition, with the significant growth of Internet-of-Things (IoT) devices, smartphones and the corresponding applications are widely considered as the Internet gateways for these devices. Mobile devices mostly use wireless LANs (WLANs) (i.e., WiFi networks) as the prominent network interface to the Internet. However, due to the broadcast nature of WiFi links, wireless traffics are exposed to any eavesdropping adversary within the WLAN. Despite WiFi encryption, studies show that application usage information could be inferred from the encrypted wireless traffic. The leakage of this sensitive information is very serious issue that will significantly impact users' privacy and security. In addressing this privacy concern, we design and develop a lightweight programmable privacy framework, called PrivacyGuard. PrivacyGuard is inspired by the vision of pushing the Software Defined Network (SDN)-like paradigm all the way to wireless network edge, is designed to support of adopting privacy preserving policies to protect the wireless communication of the sensitive applications. In this paper, we demonstrate and evaluate a prototype of PrivacyGuard framework on Android devices showing the flexibility and efficiency of the framework.

Mobility tracking of IoT devices in smart city infrastructures such as smart buildings, hospitals, shopping centers, warehouses, smart streets, and outdoor spaces has many applications. Since Bluetooth Low Energy (BLE) is available in almost every IoT device in the market nowadays, a key to localizing and tracking IoT devices is to develop an accurate ranging technique for BLE-enabled IoT devices. This is, however, a challenging feat as billions of these devices are already in use, and for pragmatic reasons, we cannot propose to modify the IoT device (a BLE peripheral) itself. Furthermore, unlike WiFi ranging - where the channel state information (CSI) is readily available and the bandwidth can be increased by stitching 2.4GHz and 5GHz bands together to achieve a high-precision ranging, an unmodified BLE peripheral provides us with only the RSSI information over a very limited bandwidth. Accurately ranging a BLE device is therefore far more challenging than other wireless standards. In this paper, we exploit characteristics of BLE protocol (e.g. frequency hopping and empty control packet transmissions) and propose a technique to directly estimate the range of a BLE peripheral from a BLE access point by multipath profiling. We discuss the theoretical foundation and conduct experiments to show that the technique achieves a 2.44m absolute range estimation error on average.

This paper proposes Muppet, an edge-based multi-protocol architecture for large-scale Internet of Things (IoT) deployment and service automation. The crux of Muppet is a P4-based switch that inserts itself in between communicating IoT devices that can use different protocols. The switches are networked over IP to support wide area deployment and managed using centralized SDN control for scalability. Muppet provides many of the benefits of both native peer-to-peer and widely used cloud-centric approaches while avoiding their drawbacks. For example, Muppet offers low-latency and low-energy benefits of the peer-to-peer approach, while enabling wide-area, cross-protocol automation similar to the cloud-based solutions. We describe the P4 design and prototype realization of the switch using two very popular, but widely disparate, IoT protocols, namely, Bluetooth low energy and Zigbee. Through experiments, we show that Muppet is as efficient as peer-to-peer in terms of delay and energy usage, and scalable and programmable as cloud-based solutions. We illustrate its utility through practical use cases.

Bluetooth Low Energy (BLE) is a personal area wireless network technology that is of increasing importance for emerging Internet of Things (IoT) deployments. By design, BLE supports short-range, single-hop communication between a pair of BLE devices. As such, native BLE does not allow network-based policy control or in-network functions for service enhancement. These limitations are impediments to any large-scale BLE based IoT deployment (e.g., in hospital environments), where such sophisticated network-based visibility and control may be required. Relying on cloud-based solutions to meet these requirements has many known shortcomings. This paper proposes an SDN-based architecture for enabling wide area IoT deployments using BLE devices at the edge. We introduce a programmable BLE service switch (BLESS) that is transparently inserted between two communicating BLE devices. BLESS can be programmed at the service layer by a central controller to enable flexible, policy-based switching, as well as various in-network operations in BLE networks. We describe the design of BLESS, its implementation using P4 and OVS, and illustrate its utility through practical use cases.

In wireless network edges, knowing the network flow types and applications can enable various policy-driven network managements (i.e. traffic offloading, BYOD, E2E QoS etc.). However, applying network policies at wireless links between the mobile devices and access points (APs) requires greater visibility and control on generated traffic generated from mobile devices. The recent advent of Software-Defined Networking (SDN) could enable fine-grained network management at the edge. However, in existing solutions, SDN uses external Deep-Packet Inspection (DPI) engine that requires additional and potentially heavy loaded computational resources to perform the packet analysis. Moreover, mobile applications become more dynamic (rapid install/update), diverse and complex (individual applications generate multiple traffic types) in which scalability and granularity requirements challenging current DPI solutions. In addition, DPI is unreliable in classifying application's encrypted packets. Therefore, in this paper we present the design and the development of TrafficVision that extends the SDN's layer architecture to have fine-grained and real-time policy making at wireless network edges. More specifically, we carefully extends the SDN framework to develop tools that allow to have scalable, efficient and flexible way to classify the network traffic flows at fine-grained fashion using Machine-Learning (ML) based technique. We evaluate our system using the performance of CPU utilization, network overhead and network throughput metrics. Finally, as a proof of concept, we develop a simple case study of traffic management application that exploits TrafficVision.

We are experiencing huge growth of video streaming traffic, which is creating big challenges for video providers in guaranteeing a satisfactory level of viewing experience to end users. Furthermore, the increase in video streaming demands on mobile devices over dynamic wireless links is creating another obstacle toward providing a high quality video service. In order to overcome most of these challenges, HTTPs adaptive video streaming technology was introduced, along with other great features for streaming videos. However, we found that HTTPs adaptive protocol can still suffer under certain situations and conditions. Mostly, these issues are likely experienced when multiple concurrent players compete over the same bottleneck. Several studies have proposed a network side solution at the home gateway or at the cloud aiming to assist the video players to maximize the viewing experience to all users sharing the same bottleneck. Although these proposed systems could provide some enhancements to the video streaming, they are unable to provide fine-grained monitoring and understanding of the video traffic to apply desire level of dynamic resource management. Considering the above issues, in this paper we conduct extensive analysis of the video traffic of YouTube; the most popular HTTPs adaptive video player. In our study, we argue that through a deep understanding and careful analysis of the HTTPs video traffic, valuable information about the competing streams can be obtained and could be utilized in developing a network based solution that can significantly improve the video QoE and assist the video players to perform much better.

In this paper, we utilize a novel communication framework Acoustic-WiFi to develop a smart contention resolution scheme Harmony among the contending devices to address the overhead of the traditional Wi-Fi backoff scheme (i.e. contention window countdown, DIFS) and reduce the overall collisions among the devices. Harmony uses the acoustic channel for contention resolution in Wi-Fi networks. To the best of our knowledge, Harmony is the first to leverage the acoustic interface on commodity smart devices as an addition control channel in parallel with the Wi-Fi interface. We evaluate our scheme using real testbed and simulation. Testbed experiments show more than 40% throughput gain over traditional Wi-Fi networks, while simulation results show more than 27% gain for dense networks.

In this paper, we design, implement and evaluate the SpyLoc localization system. The design goal of SpyLoc is to develop a light weight and high accuracy localization system for off-the-shelf smartphones. SpyLoc leverages both the acoustic interface (microphone/speaker) and the Wi-Fi interface at the kernel-level of smartphones as well as the inertial sensors in smartphones to achieve high localization accuracy. SpyLoc does not require any central controller unit nor any collaboration with nearby devices. Furthermore, in SpyLoc, each user's smartphone works autonomously to estimate its location. We implement and evaluated the complete SpyLoc using commercial off-the-shelf smartphones. Our result shows that SpyLoc can achieve less then 1 meter accuracy for more than 90% of the time for both indoor and outdoor environments.

Mobile devices such as smartphones have a number of sensors that can be exploited to solve a number of problems related to health care delivery. In this paper we use accelerometer, gyroscope, and compass sensors on smart-phones to solve a location tracking problem common to many emergency departments. An emergency department typically has unique characteristics including not being friendly to be visually surveyed, since the layout consists of many isolated islands and the workstation layout is not standardized. The spaghetti diagram is a tool to help to identify areas where time can be saved by visualizing unnecessary movement of products, staff or patients. We report the development of an automated tool to create spaghetti diagrams of movements of personnel in a non-intrusive way. A preliminary prototype to produce paths showed very encouraging results. We also identify challenges and our approach to meet them.

In this paper, we propose and addresses the challenge of designing a light-weight high-accuracy indoor/outdoor localization system (SpyLoc) for off-the-shelf smartphones. In SpyLoc, we want to leverages both the acoustic interface (microphone/speaker) and the Wi-Fi interface at the kernel-level of the smartphones as well as the inertial sensors in the smartphones to achieve high localization accuracy. In the ranging-based approach, we utilizes the RF-Beep ranging scheme[?], and in dead reckoning-based approach, we fuses the inertial sensors of the smartphone to estimate the direction and the distance of user's movements. In RF-Beep [?], we develope a ranging scheme that utilizes the Time-Difference-of-Arrival (TDoA) between the acoustic and the radio-frequency (RF) signal. The RF-Beep basically leverages the slow propagation speed of the acoustic signal with respect to the RF signal to estimate the relative range. The well known acoustic range based localization scheme, Cricket [?] also utilizes the same concept of using the difference in arrival times of concurrent transmissions of radio and ultrasound signals at the target device to infer the distance. Unlike the Cricket, which was designed with special hardware, our localization scheme is applicable to the smartphones. In RF-Beep, we address the different challenges of implementing such ranging scheme in smartphones by leveraging the existing functionalities of the audio driver and the WiFi driver. Further details on RF-Beep scheme could be found in~\cite{Uddin:RF}. The ranging based localization scheme typically requires at least three reference points (e.g., anchor points), in order to calculate the location. However all these three reference points must be in Line of Sight(LoS) to the target device. In \textit{SpyLoc} localization system, we use the combination of both ranging-based and dead reckoning approaches to reduce the constraint of having three LoS anchor/reference points all the time. The basic idea of the SpyLoc is to leverage the benefits of both the dead-reckoning and the ranging scheme to build a practical localization system. Given the high errors of the inertial sensors, SpyLoc uses a novel ranging scheme based on both the acoustic and WiFi interfaces to mitigate this error in order to improve the localization accuracy. Unlike the ranging-based or RF-based localization schemes that require multiple reference points (e.g., achor points), using the dead reckoning in SpyLoc reduces the required number of reference points to only one reference to locate and track users accurately. This low dependency on ranging scheme make SpyLoc practically applicable to high mobility environment. Finally, In the SpyLoc system, user's device (i.e. smartphone) works autonomously to determine its location. This system does not require any coordination from the nearby smartphones or from a central controller. Furthermore, this localization system does not require user's device to transmit any acoustic signal or RF messages to nearby smartphones. Therefore increasing the number of user's devices have no impact on the complexity of the SpyLoc system, which make SpyLoc a light-weight localization system. In addition, such characteristics make the localization system privacy preserving and energy efficient for the user's smartphone.

In this paper, we design, implement and evaluate RF-Beep - a high-accuracy, one-way sensing, energy efficient and light-weight ranging scheme for smart devices. RF-Beep is based on the well known Time-Difference-of-Arrival (TDoA) scheme that utilizes the different propagation speeds of both the acoustic and the radio-frequency (RF) signals. Unlike the previous works, RF-Beep utilizes both the audio interface (i.e., microphone, speaker and sound driver) and the RF interface (i.e., WiFi) at the kernel-level of commercial-off-the-shelf smart devices. Implementing the scheme at lower levels enables us to understand and address the challenges related to the timing uncertainties in transmitting and receiving the acoustic signal. Moreover, RF-Beep does not require any special hardware or infrastructure support. In this paper, we describe the complete implementation of RF-Beep at the kernel space of Linux OS. We evaluate RF-Beep under different indoor and outdoor real scenarios. Results show that the error in the estimated range is less than 50cm for more than 93% of the time.

In this paper we present the design and the evaluation of a framework MagnoTricorder, a system that utilizes the magnetic sensor in smartphones to detect the running devices at home thru a singlepoint sensing. MagnoTricorder leverages the effect of Electro Magnetic Interference (EMI) generated by the AC current in the main power-line at home. This EMI induces a magnetic field that highly fluctuates the reading of the magnetic sensor in smartphones. In this paper, we utilize this characteristic for detecting and identifying the running devices at home thru the Circuit Breaker Panel. Experimental evaluation demonstrates the feasibility of the developed framework. Results show that MangoTricorder can detect and identify individual devices with 93%-98% accuracy.

Tracking energy consumption for individual operating machines (e.g., home appliance) is a prerequisite for making energy conservation and management efficient. In order to meet the requirement for monitoring home energy consumption, several industries and researchers came up with different solutions. Unfortunately, all these solutions require invasive and expensive installation of sensor devices. Furthermore, many of these solutions can't measure the energy consumption of individual machines. In this paper, we propose and evaluate the feasibility of using smart phones in machines's energy monitoring system. We call our system EnergySniffer in which it exploits various sensors, such as magnetic sensor, light, microphone, temperature, camera, WiFi, in smart phones to build a multi sensing framework. This framework is used to build a unique fingerprint profile for each individual machine. As a proof of concept, we develop a simple sensing framework prototype that utilizes only the microphone sensor on the phone. We call this framework a sound sensing framework. Experimental evaluation on sound sensing framework demonstrate the feasibility of continuously identifying and monitoring individual machine in real-time.