Mostafa Uddin

meSDN: mobile extension of SDN

Mobile devices interact wirelessly with a growing proliferation of cloud-based applications. Due to significant traffic growth and a wide variety of multimedia solutions, enterprise IT departments are demanding more fine-grained visibility and control of mobile traffic. They want to deliver optimal performance and a high quality of experience to a variety of users and applications. In the wired world, Software-Defined Networking (SDN) is a technology being embraced to deliver performance guarantees to end users by dynamically orchestrating quality of service (QoS) policies on edge switches and routers. Guaranteeing performance in a wired access network does not require any network control on clients, because the last hop between the network edge and wired device is a dedicated point-to-point link (e.g. Ethernet). However, this is not the case with wireless LANs (WLAN), since the last hop is a shared half-duplex medium and the WiFi MAC protocol does not allow access points to coordinate client uplink transmissions or 802.11 QoS settings. Hence, we argue that the SDN paradigm needs to be extended to mobile clients to provide optimal network performance between the cloud and wirelessly-connected clients. In this paper, we propose a framework called meSDN and demonstrate that it enables WLAN virtualization, application-aware QoS and improves power-efficiency from our prototype on Android phones.

A2PSM: audio assisted wi-fi power saving mechanism for smart devices

Wi-Fi is the most prominent wireless network interface in current smart devices. Due to its high power consumption, Power Saving Mode (PSM) schemes have been proposed to reduce power consumption. We show how the current popular PSM schemes implemented in nowadays smart devices are inefficient. In this paper, we propose A2PSM: an audio channel assisted power saving scheme for the Wi-Fi interface, which address the inefficiency of the existing power saving schemes in smart devices. In this scheme, we leverage the low power consumption of the audio interfaces (mic/speaker) to reduce the wakeup events of the Wi-Fi interface when it is in Power Saving Mode. In this paper, we develop a small-scale prototype testbed on real smartphones to evaluate the proposed A2PSM scheme. Experiments show that A2PSM could save up to more than 25% more power than the existing schemes. To the best of our knowledge, this is the first work to utilize the audio channel in optimizing the power consumption of Wi-Fi networks.

RF-Beep: A light ranging scheme for smart devices

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.

SpyLoc: a light weight localization system for smartphones

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 users 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.

MagnoTricorder: what you need to do before leaving home

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.

MachineSense: detecting and monitoring active machines using smart phone

Comparison of actual running machine and recognized running machine using our prototype system for 25 minutes of time. Multivariate Gaussian Acoustic Model Naive Bayes classifier with equal prior probability Circle 1,2,3 shows the position of a microwave, a fan and a vacuum cleaner respectively at our lab office. Square 1,2,3,4 and 5 shows the position from we have identified the current running machine using our prototype application. We develop a prototype system in Android phone (Nexus S)

Understanding the intermittent traffic pattern of HTTP video streaming over wireless networks

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.

SmartSpaghetti: Accurate and robust tracking of Human's location

An important tool of the Lean management is the “Spaghetti Diagram”, which helps to establish the optimum layout for a department or a ward based on the movements performed by patients, staff and/or products (e.g., x-ray machines). The spaghetti diagram is usually created manually in which the movements of staff members and/or patients are visually observed. In our previous work, we reported the development of SmartSpaghetti system, which generates the Spaghetti Diagram in an automated and non-intrusive way by tracking a humans location using the smartphones inertia sensors. In this paper, we address the challenges of SmartSpaghetti system in tracking humans location by estimating the distance traveled and the direction change in an accurate and robust way. Finally, we show preliminary evaluation of the developed distance and direction estimation modules of SmartSpaghetti system.

TrafficVision: A Case for Pushing Software Defined Networks to Wireless Edges

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 applications encrypted packets. Therefore, in this paper we present the design and the development of TrafficVision that extends the SDNs 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.

Harmony: Content resolution for smart devices using acoustic channel

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.