Sugang Li

• I am starting to work at Google (03/22/2021)


• Our paper "“Energy-Ball: Wireless Power Transfer for Batteryless Internet of Things through Distributed Beamforming" has been accepted by Ubicomp/IMWUT 2018!


• Our paper "Monitoring a Person’s Heart Rate and Respiratory Rate on a Shared Bed Using Geophones" has been accepted by SenSys 2017!Paper


• Our paper "Auto++:Detecting Cars Using Embedded Microphones in Real-Time" has been accepted by Ubicomp/IMWUT 2017! See you guys in Hawaii! Paper Slides.


• Our demo "Motion-Triggered Surveillance Camera using MF-IoT" has been rewarded as Best Demo at IoTDI'17. Demo Poster. Paper


• I am joining AT&T Research Center for the Summer Intern!


• I presented our work on PerCom 2016 on March 16th. You can find the slides and the demo poster here.


• Our paper "Whose Move is it Anyway? Authenticating Smart Wearable Devices Using Unique Head Movement Patterns" has been accepted by PerCom 2016! You can find the paper here.

MF-IoT: MF-IoT enables efficient communication between devices from different local domains, as well as communication between devices and the infrastructure network. The MF-IoT network layer smoothly handles mobility during communication without interrupting the applications. This is achieved through a transparent translation mechanism at the gateway node that bridges an IoT domain and the core network. In the core network, we leverage MobilityFirst functionalities in providing efficient mobility support for billions of mobile devices through long, persistent IDs, while in the local IoT domain, we use short, local IDs for energy efficiency. By seamlessly translating between the two types of IDs, the gateway organically stitches these two parts of the network.

AggMEC:In the era of IoT and 5G network, the number of connected IoT devices has been growing dramatically, while most of them only transmit packets in small size. This new traffic pattern imposes significant challenges to the network infrastructures as well as the computing platforms. Aggregating multiple packets which have the same destination and similar attributes at the edge network can efficiently reduce the data traffic and the overhead. In this work, we propose a comprehensive framework which unveils relations between the aggregated data traffic and the system parameters(eg., the number of aggregation nodes and the waiting time at each aggregation node) in general networks. We further introduce two heuristic algorithm to allocate a limited number of aggregation nodes into the edge network in order to decrease the total network traffic. The former aims at placing a pre-defined number of computing nodes while the latter is seeking the traffic hot-spots in the network to determine the locations of the computing nodes. Through large-scale network simulations over real-world data traces, we show that our first heuristic algorithm can reduce 80% of the total traffic from the baseline solution, and the second heuristic algorithm can effectively identify the traffic hot-spots hence achieve better aggregation efficiency than the constant-computing-node-number schemes.

Hetero-Edge:As mobile devices continue to generate large volumes of data, a new family of edge applications is rapidly looming on the surface, such as augmented/virtual reality, autonomous driving, etc. Running computer vision algorithms on images or videos collected by mobile devices represent a new class of latency-sensitive applications that expect to benefit from edge cloud computing. These applications often demand real-time responses (e.g., less than 100 ms), which can not be satisfied by traditional cloud computing. However, the edge cloud architecture is inherently distributed and heterogeneous, requiring new approaches to resource allocation and orchestration. This paper presents the design and evaluation of a latency-aware edge computing platform, aiming to minimize the end-to-end latency for edge applications. The proposed platform is built on Apache Storm, and consists of multiple edge servers with heterogeneous computation (including both GPUs and CPUs) and networking resources. Central to our platform is an orchestration framework that breaks down an edge application into Storm tasks as defined by a directed acyclic graph (DAG) and then maps these tasks onto heterogeneous edge servers for efficient execution. An experimental proof-of-concept testbed is used to demonstrate that the proposed platform can indeed achieve low end-to-end latency: considering a real-time 3D scene reconstruction application, it is shown that the testbed can support up to 30 concurrent streams with an average per-frame latency of 32ms, and can achieve 40% latency reduction relative to the baseline Storm scheduling approach.

Auto++:In this work, we propose a system that detects approaching cars for smartphone users. In addition to detecting the presence of a vehicle, it can also estimate the vehicle’s driving direction, as well as count the number of cars around the user. We achieve these goals by processing the acoustic signal captured by microphones embedded in the user’s mobile phone. The largest challenge we faced involved addressing the fact that vehicular noise is predominantly due to tire-road friction, and therefore lacked strong (frequency) formant or temporal structure. Additionally, outdoor environments have complex acoustic noise characteristics, which are made worse when the signal is captured by non-professional grade microphones embedded in smartphones. We address these challenges by monitoring a new feature: maximal frequency component that crosses a threshold. We extract this feature with a blurred edge detector. Through detailed experiments, we found our system to be robust across different vehicles and environmental conditions, and thereby support unsupervised car detection and counting.

Headbanger: We propose a system for direct authentication of users to their head--worn wearable device through a novel approach that identifies users based on motion signatures extracted from their head--movements. This approach is in contrast to existing indirect authentication solutions via smartphone or using touch--pad swipe patterns. The system, dubbed software, is a software authentication solution that leverages unique motion patterns created when users shake their head in response to music played on the head--worn device, and sensed through integrated accelerometers.

Crowdpp: Smartphones are excellent mobile sensing platforms, with the microphone in particular being exercised in several audio inference applications. We take smartphone audio inference a step further and demonstrate for the first time that it’s possible to accurately estimate the number of people talking in a certain place – with an average error distance of 1.5 speakers – through unsupervised machine learning analysis on audio segments captured by the smartphones. Inference occurs transparently to the user and no human intervention is needed to derive the classification model. Our results are based on the design, implementation, and evaluation of a system called Crowd++, involving 120 participants in 10 very different environments. We show that no dedicated external hardware or cumbersome supervised learning approaches are needed but only off-the-shelf smartphones used in a transparent manner. We believe our findings have profound implications in many research fields, including social sensing and personal wellbeing assessment. Currently, we are collaborating with RU Psychology Department on stuyding the relation between children's daily social interaction and Autism.