Our objective is to enable stateful applications on the serverless architecture. A stateful application is characterized by a function-chain. The need for maintaining the state arises because the platform that is executing the function-chain must take the output from any given function in the chain, hold it until the next function in the chain starts execution, and provide it to that function when the execution starts. Thus, towards achieving the objective of enabling stateful applications on the serverless architecture, we envision two major tasks that we plan to undertake. First, we will extend our existing serverless platform to support function-chains in addition to standalone stateless functions.Second, we will demonstrate the feasibility of the framework resulting from the first task by executing selected existing stateful applications on this framework.

In this project, we propose to develop methods and protocols to automatically onboard BLE devices so that they can connect to any available network and be able to exchange the desired information. We will further develop methods to utilize metadata for analytics and implement appropriate access control policies so that entities with proper permissions can get timely access to the right type of data.

Today's cloud data centers host diverse workloads, where multiple customer applications running in the cloud can send processing requests to multiple servers in that cloud. Due to this diversity, both the server and network resources need protection against heavy hitters. To enable this resource protection, we plan to augment methods from the conventional control theory with the modern machine learning to design feedback based closed-loop methods that shape traffic between multiple source and destination servers.

In this project, we propose to develop methods to enable serverless computing at the edge. Our primary objectives include developing methods to cache serverless functions within the edge network and to optimally schedule functions and function-chains while satisfying the latency SLAs. Our secondary objectives include studying the feasibility of enabling stateful applications on serverless platforms and customizing serverless edge for certain targeted applications.

In this project, our objective is to use radio frequency signals to generate indoor maps of any given building without entering it. In generating these maps, our secondary objective is to discover any humans that are present in the building, identify their locations, and determine which of them are stationary and which are mobile.

PI proposes to develop a framework for passive and fine-grained measurements of the performance metrics in the Internet of Things, which include both Quality of Service metrics such as latency, loss, and throughput and Resource Utilization metrics such as power consumption, storage utilization, and radio on time etc. Measurements of these performance metrics can be used reactively by network operators to perform tasks such as detecting and localizing offending flows that are responsible for causing delay bursts, throughput deterioration, or even power surges. These measurements can also be used proactively by network operators to locate and preemptively update any potential bottlenecks.

In this project, our objective is to use WiFi������������������s wireless channel metrics to generate indoor map of any given building without entering it. In generating these maps, our secondary objective is to discover any humans that are present in the building, identify their locations, and determine which of them are stationary and which are mobile.

Due to the rapid adoption of the cloud computing model, the size of the data centers and the variety of the cloud services is increasing at an unprecedented rate. Due to this, fine-grained monitoring of the health and the usage of data center resources is becoming increasingly important and challenging. In this work, we address the problem of efficiently acquiring and transporting cloud management and monitoring data. For data acquisition, we address the crucial challenge of controlling data size. For data transportation, we focus on efficiently moving the data from the point it is collected inside the data center to the point it needs to be stored for processing.

In this project, we propose to perform indoor position tracking using inertial sensors that are already built into most commodity handheld and wearable devices. We plan to improve the accuracy of the position tracking by leveraging the ambient WiFI signals already present in most modern buildings.

The PI proposes to use ambient light for recognizing human gestures. The intuition behind the proposed approach is that as a user performs a gesture in a room that is lit with light, the amount of light that he/she reflects and blocks changes, resulting in a change in the intensity of light in all parts of the room. This change can be measured and the pattern of change in the intensity of light is different for different gestures. Leveraging this observation, the proposed approach first learns these patterns for different gestures and then recognizes the gestures in real-time.