Ongoing Research Projects

My current reserach focuses on Computing and Communications in various Internet-of-Things paradigms. My recent research efforts are described into the following themes:

Collaborative Perception over Vehicular Edge Computing for Smart Transportation


In smart transportation systems, the local perception based on one vehicle’s sensor data may not yield high accurate global perception and might not benefit the legacy vehicles. Multi-sensor fusion can greatly improve the vehicular perception by collaborative computations with the help of edge server. However, the V2X communication over wireless channel can impact due to high mobility and handover, as it may require handing over the last state of the computation. We aim to create efficient task partitioning algorithm between vehicle(s) and road side edge computing nodes and real-time data multi-sensor (e.g., multi-cameras, multi-radars) fusion algorithm at the edge to improve the accuracy of collaborative vision. We are building a prototype testbed containing smart vehicles, small cell radios for C-V2X communication and edge computing nodes. In collaboration with the cities we plan to deploy the distributed testbeds and further develop predictive models from collected data for proactive handovers that is resilient to computation loss.

STIP Project, DAC 2020, STIP_Demo    

Collaborators: Samuel Thornton (UCSD), Yujen Ku (UCSD), Sujit Dey (UCSD)

Design Space Exploration for Machine Learning Applications on Embedded Systems


In complex computing-intensive tasks for AI-driven systems, the performance of the application and system needs to be jointly evaluated. In order to optimize the power, area and speed of the systems along with high accuracy and low latency of the applications, e.g. deep learning algorithms, we need to decompose the complex algorithms into domain specific functions (DSF). The granularity of the DSFs depends on the interoperability of the functions on different devices as well as their reusability in different machine learning algorithms. The DSFs are needed to be profiled on different systems to understand the best device for a specific DSF and algorithm. Once the DSFs are defined and profiled, we will need to formulate multi-objective optimization problems based on performance of different components on different software and hardware platforms, e.g., CPU, GPU and accelerators. We are working on this project to conduct a design-space exploration for software-hardware co-design for an application-driven adaptive and efficient computing systems.

AISS Project  

Collaborators: Sujit Dey (UCSD), Anand Raghunathan (Purdue University)

Resilient Computation for Heterogeneous Autonomous Drone Systems

The ability to execute complex signal processing and machine learning tasks in real-time is the core of autonomy. In airborne devices such as Unmanned Aerial Vehicles (UAV), the hardware limitations imposed by the weight constraint make the continuous execution of these algorithms challenging. Edge and fog computing can mitigate such limitations and boost the system and mission-level performance of the UAVs. However, due to the UAVs motion characteristics and complex dynamics of urban environments, the performance of pipelines using interconnected, rather than onboard, resources can quickly degrade. We are designing and building a system called "Hydra", an architecture for the establishment of flexible sensing-analysis-controlpipelines over autonomous airborne systems. We introduce the concept of information autonomy to extend autonomy to encompass how  information-related  tasks  are  handled  in  this  challenging  scenarios.  We motivate the need for “Information Autonomy” based on  our  observations  from  real-world  experiments  and build a  self-adaptive  framework  for  edge-assisted  UAV  applications.

ICC 2020, Mobicom HotEdgeVideo 2019, MILCOM 2019Sigcomm MAGESys 2019       

Collaborators: Davide Callegaro (UCI), Yoshitomo Matsubara (UCI), Marco Levorato (UCI), Gowri Ramachandran (USC), Bhaskar Krishnamachari (USC)

Renewable Energy-driven Resource-efficient Vehicular Edge Computing



Since base stations (BSs) consume 80% of the total power in cellular networks, the need to reduce the power consumption of the BSs is crucial for energy-efficient cellular networks. Renewable energy (RE) is a promising solution to reduce grid energy consumption and carbon emissions of BSs. However, the benefit of utilizing Renewable Energy (RE) is limited by its highly intermittent and unreliable nature, resulting in low savings in grid energy. Our objective is to maximize utilization of harvested RE at the BSs with minimal use of energy storage (battery), thereby enabling the use of renewable energy feasible at low-cost. With the RE-powered Small Cell (SC) testbed, we aim at developing a machine learning model to predict the remaining operation time of a small cell based on the current system and demand status. The feature set includes historical communication traffic, computing demand, solar and wind energy generation profile, environmental factors such as temperature and humidity.

Project 

Collaborators: Yujen Ku (UCSD), Sujit Dey (UCSD)




Past Research Projects

Robust Communications for Unmanned Aerial Vehicles (UAV)


Resilient UAV Communication

Unmanned Aerial Vehicle (UAV) systems are being increasingly used in a broad range of scenarios and applications. However, their deployment in urban areas poses important technical challenges in communications between the ground  stations and the UAVs in a highly dynamic and crowded spectrum. Indeed, competing data streams may create local or temporary congestion impairing the ground stations to control the UAVs. We design  a robust multi-path communication framework for  UAV systems.  The framework continuously probes the performance of multiple wireless multi-hop paths from the ground stations to each UAV, and dynamically selects the path providing the best performance to support timely control. Based on a real-world implementation and extensive field experimentation with 3DR Solo drone, we demonstrate the ability of the proposed framework to provide robust control against exogenous interference and network congestion. We also created a fully open-sourced intergrated UAV-network simulator called "FlyNestSim" to simulate wide range of UAV communication scenarios.

DCOSS 2020, SECON CPC-UAV 2018, MSWiM 2018, FlyNetSim Code     

Collaborators : Zoheb Shaikh (UCI), Marco Levorato (UCI)

Software-Defined Real-time Edge Computing using Network Function Virtualization (NFV)


eBPF architecture

By placing computation resources within one-hop wireless topology, the recent edge computing paradigm is a keyenabler of real-time Internet of Things (IoT) applications. In the context of IoT scenarios where the same information from a sensor is used by multiple applications at different locations, the data stream needs to be replicated. However, the transportation of parallel streams might not be feasible due to limitations in the capacity of the network transporting the data. To address  this issue, a content and computation-aware communication control framework is proposed based on the  Software Defined Network(SDN) paradigm. The framework supports multi-streaming using the extended Berkeley Packet Filter (eBPF), where the traffic flow and packet replication for each  specific computation process is controlled by a program running inside an in-kernel Virtual Machine (VM). This reduces the amount of data to be forwarded in the higher layer, and minimizes the socket buffer allocation; thus, saving both system resources and latency.

INFOCOM SCAN 2018, INFOCOM SCAN 2017eBPF-cast Code

Collaborators : Yan Chen (Huawei Research Lab, CA), Marco Levorato (UCI)

Content-aware Cognitive Interference Control for Urban IoT Systems



We propose a novel cognitive interference control framework for heterogeneous local access networks supporting computingand data processing in urban Internet of Things (IoT) systems. The notion of cognitive content-aware interference control is introduced, where the transmission pattern of cognitive nodes is dynamically adapted to the state of “protected” IoT data streams. The state describes the performance degradation that interference would cause to algorithms processing the data if the cognitive nodes chose to transmit in the corresponding time period. The framework is instantiated for a case-study scenario, where device-to-device (D2D) and long-term evolution communications coexist on the same channel resource. A city-monitoring application is considered, where the state captures the type of frames within a multimedia video stream processed at the edge of the network to detect and track objects. The proposed transmission strategy significantly increase throughput of local D2D communications for a target accuracy of the monitoring application. 

IEEE TCCN 2018, Springer 2018ICNC 2017,  Globecom 2016         

Collaborator : Marco Levorato (UCI)

Adaptive Communications over Multipath and Multi-Networks


spie

Multipath Communications is one of the key enabler to improve the data throughput and hence the performance of the applications. If multiple paths are used simultaneously, e.g., Multipath TCP (MPTCP), the assymetry in the quality of different paths can cause out-of-order packets and hence the buffer boat problem which affects the latency and jitter of the application. We propose slow-path adaptation technique that monitors quality of all the available paths, and based on threshold decides to selectively retransmit delayed packets over faster path while reducing the transmission rate on the slow-path to the minimum until its quality improves. It improves the MPTCP performance in high asymmetric conditions and never falls below achievable TCP level throughput. In case of complex and real-time applications, selecting the best path can yields better result. By incorporating data-driven prediction mechanism, we select the best available paths dynamically for real-time applications without any significant affect on the monitored paths.

ICC EML5G 2020, IEEE ICC 2014, UTD-CS 2013      

Collaborators : Peyman Tehrani (UCI), Marco Levorato (UCI), Promod Shirol (UT Dallas), Abhishek Basu (UT Dallas), Dr. Ravi Prakash (UT Dallas)