Jaya Prakash Champati

University of Victoria | UVIC · Department of Computer Science

The freshness of sensor data is critical for all types of cyber-physical systems. An established measure for quantifying data freshness is the Age-of-Information (AoI), which has been the subject of extensive research. Recently, there has been increased interest in multi-sensor systems: redundant sensors producing samples of the same physical proce...

Hierarchical Inference (HI) has emerged as a promising approach for efficient distributed inference between end devices deployed with small pre-trained Deep Learning (DL) models and edge/cloud servers running large DL models. Under HI, a device uses the local DL model to perform inference on the data samples it collects, and only the data samples o...

Epidemiologists and social scientists have used the Network Scale-Up Method (NSUM) for over thirty years to estimate the size of a hidden sub-population within a social network. This method involves querying a subset of network nodes about the number of their neighbours belonging to the hidden sub-population. In general, NSUM assumes that the socia...

On-device inference holds great potential for increased energy efficiency, responsiveness, and privacy in edge ML systems. However, due to less capable ML models that can be embedded in resource-limited devices, use cases are limited to simple inference tasks such as visual keyword spotting, gesture recognition, and predictive analytics. In this co...

The Hierarchical Inference (HI) paradigm employs a tiered processing: the inference from simple data samples are accepted at the end device, while complex data samples are offloaded to the central servers. HI has recently emerged as an effective method for balancing inference accuracy, data processing, transmission throughput, and offloading cost....

We consider a resource-constrained Edge Device (ED), such as an IoT sensor or a microcontroller unit, embedded with a small-size ML model (S-ML) for a generic classification application and an Edge Server (ES) that hosts a large-size ML model (L-ML). Since the inference accuracy of S-ML is lower than that of the L-ML, offloading all the data sample...

In this paper, we evaluate the performance and analyze the explainability of machine learning models boosted by feature selection in predicting COVID-19-positive cases from self-reported information. In essence, this work describes a methodology to identify COVID-19 infections that considers the large amount of information collected by the Universi...

With the emergence of edge computing, the problem of offloading jobs between an Edge Device (ED) and an Edge Server (ES) received significant attention in the past. Motivated by the fact that an increasing number of applications are using Machine Learning (ML) inference from the data samples collected at the EDs, we study the problem of offloading...

Resource-constrained Edge Devices (EDs), e.g., IoT sensors andmicrocontroller units, are expected to make intelligent decisionsusing Deep Learning (DL) inference at the edge of the network.Toward this end, developing tinyML models is an area of active re-search – DL models with reduced computation and memory storagerequirements – that can be embedd...

In this paper, we introduce a machine-learning approach to detecting COVID-19-positive cases from self-reported information. Specifically, the proposed method builds a tree-based binary classification model that includes a recursive feature elimination step. Based on Shapley values, the recursive feature elimination method preserves the most releva...

Background: During the global pandemic crisis, various detection methods of COVID-19-positive cases based on self-reported information were introduced to provide quick diagnosis tools for effectively planning and managing healthcare resources. These methods typically identify positive cases based on a particular combination of symptoms, and they h...

Resource-constrained Edge Devices (EDs), e.g., IoT sensors and microcontroller units, are expected to make intelligent decisions using Deep Learning (DL) inference at the edge of the network. Toward this end, there is a significant research effort in developing tinyML models - Deep Learning (DL) models with reduced computation and memory storage re...

We consider a resource-constrained Edge Device (ED) embedded with a small-size ML model (S-ML) for a generic classification application, and an Edge Server (ES) that hosts a large-size ML model (L-ML). Since the inference accuracy of S-ML is lower than that of the L-ML, offloading all the data samples to the ES results in high inference accuracy, b...

This paper contributes tail bounds of the age-of-information of a general class of parallel systems and explores their potential. Parallel systems arise in relevant cases, such as in multi-band mobile networks, multi-technology wireless access, or multi-path protocols, just to name a few. Typically, control over each communication channel is limite...

Symptoms-based detection of SARS-CoV-2 infection is not a substitute for precise diagnostic tests but can provide insight into the likely level of infection in a given population. This study uses symptoms data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID. This work, conduc...

This paper contributes tail bounds of the age-of-information of a general class of parallel systems and explores their potential. Parallel systems arise in relevant cases, such as in multi-band mobile networks, multi-technology wireless access, or multi-path protocols, just to name a few. Typically, control over each communication channel is limite...

Wearable Cognitive Assistance (WCA) applications present a challenge to benchmark and characterize due to their human-in-the-loop nature. Employing user testing to optimize system parameters is generally not feasible, given the scope of the problem and the number of observations needed to detect small but important effects in controlled experiments...

Introduction: Having accurate and timely data on active COVID-19 cases is challenging, since it depends on the availability of an appropriate infrastructure to perform tests and aggregate their results. In this work, we propose alternative methods to assess the number of active cases of COVID-19. Methods: We consider a case to be active if it is i...

Smart IoT-based systems often desire continuous execution of multiple latency-sensitive Deep Learning (DL) applications. The edge servers serve as the cornerstone of such IoT-based systems, however, their resource limitations hamper the continuous execution of multiple (multi-tenant) DL applications. The challenge is that, DL applications function...

Multiple COVID-19 diagnosis methods based on information collected from patients have been proposed during the global pandemic crisis, with the aim of providing medical staff with quick diagnosis tools to efficiently plan and manage the limited healthcare resources. In general, these methods have been developed to detect COVID-19 positive cases fro...

We consider a finite-state Discrete-Time Markov Chain (DTMC) source that can be sampled for detecting the events when the DTMC transits to a new state. Our goal is to study the trade-off between sampling frequency and staleness in detecting the events. We argue that, for the problem at hand, using Age of Information (AoI) for quantifying the stalen...

Data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID, are used to evaluate the impact of the Omicron variant (in South Africa and other countries) on the prevalence of COVID-19 among unvaccinated and vaccinated population, in general and discriminating by the number of doses....

Data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID, are used to evaluate the impact of the Omicron variant (in SouthAfrica and other countries) on the prevalence of COVID-19 among unvaccinated and vaccinated population, in general and discriminating by the number of doses....

We study the problem of finding efficient sampling policies in an edge-based feedback system, where sensor samples are offloaded to a back-end server that processes them and generates feedback to a user. Sampling the system at maximum frequency results in the detection of events of interest with minimum delay but incurs higher energy costs due to t...

With the emergence of edge computing, the problem of offloading jobs between an Edge Device (ED) and an Edge Server (ES) received significant attention in the past. Motivated by the fact that an increasing number of applications are using Machine Learning (ML) inference, we study the problem of offloading inference jobs by considering the following...

We study the problem of finding efficient sampling policies in an edge-based feedback system, where sensor samples are offloaded to a back-end server that processes them and generates feedback to a user. Sampling the system at maximum frequency results in the detection of events of interest with minimum delay but incurs higher energy costs due to t...

Having accurate and timely data on confirmed active COVID-19 cases is challenging, since it depends on testing capacity and the availability of an appropriate infrastructure to perform tests and aggregate their results. In this paper, we propose methods to estimate the number of active cases of COVID-19 from the official data (of confirmed cases an...

Interactive applications with automated feedback will largely influence the design of future networked infrastructures. In such applications, status information about an environment of interest is captured and forwarded to a compute node, which analyzes the information and generates a feedback message. Timely processing and forwarding must ensure t...

There is a growing interest in analysing freshness of data in networked systems. Age of Information (AoI) has emerged as a relevant metric to quantify this freshness at a receiver, and minimizing this metric for different system models has received significant research attention. However, a fundamental question remains: what is the minimum achievab...

Age of Information (AoI) has proven to be a useful metric in networked systems where timely information updates are of importance. In the literature, minimizing “average age” has received considerable attention. However, various applications pose stricter age requirements on the updates which demand knowledge of the AoI distribution. Furthermore, t...

In the design of multi-loop Networked Control Systems (NCSs), wherein each control system is characterized by heterogeneous dynamics and associated with a certain set of timing specifications, appropriate metrics need to be employed for the synthesis of control and networking policies to efficiently respond to the requirements of each control loop....

We study task scheduling and offloading in a cloud computing system with multiple users where tasks have different processing times, release times, communication times, and weights. Each user may schedule a task locally or offload it to a shared cloud with heterogeneous processors by paying a price for the resource usage. We consider four different...

In the design of multi-loop Networked Control Systems (NCSs) wherein each control system is characterized by heterogeneous dynamics and associated with certain set of timing specifications and constraints, appropriate metrics need to be employed for the synthesis of control and networking policies to efficiently respond to the requirements of each...

We consider a finite-state Discrete-Time Markov Chain (DTMC) source that can be sampled for detecting the events when the DTMC transits to a new state. Our goal is to study the trade-off between sampling frequency and staleness in detecting the events. We argue that, for the problem at hand, using Age of Information (AoI) for quantifying the stalen...

In this article, we investigate the transient behavior of a sequence of packets/bits traversing a multi-hop wireless network under static routing. Our work is motivated by novel applications from the domain of process automation, Machine-Type Communication (MTC) and cyber-physical systems, where short messages are communicated and statistical guara...

We consider a finite-state Discrete-Time Markov Chain (DTMC) source that can be sampled for detecting the events when the DTMC transits to a new state. Our goal is to study the trade-off between sampling frequency and staleness in detecting the events. We argue that, for the problem at hand, using Age of Information (AoI) for quantifying the stalen...

There is a growing interest in analysing the freshness of data in networked systems. Age of Information (AoI) has emerged as a popular metric to quantify this freshness at a given destination. There has been a significant research effort in optimizing this metric in communication and networking systems under different settings. In contrast to previ...

There is a growing interest in analysing the freshness of data in networked systems. Age of Information (AoI) has emerged as a popular metric to quantify this freshness at a given destination. There has been a significant research effort in optimizing this metric in communication and networking systems under different settings. In contrast to previ...

Age of Information (AoI) has proven to be a useful metric in networked systems where timely information updates are of importance. In the literature, minimizing "average age" has received considerable attention. However, various applications pose stricter age requirements on the updates which demand knowledge of the AoI distribution. Furthermore, t...

We study the problem of scheduling $n$ tasks on $m + m^\prime$ parallel processors, where the processing times on $m$ processors are known while those on the remaining $m^\prime$ processors are not known a priori. This semi-online model is an abstraction of certain heterogeneous computing systems, e.g., with the $m$ known processors repre...

Computational offloading systems, where computational tasks can be processed locally or offloaded to a remote cloud, have become prevalent since the advent of cloud computing. The task scheduler in a computational offloading system decides both the selection of tasks to be offloaded to the remote cloud and the scheduling of tasks on the local proce...

Since Age of Information (AoI) has been proposed as a metric that quantifies the freshness of information updates in a communication system, there has been a constant effort in understanding and optimizing different statistics of the AoI process for classical queueing systems. In addition to classical queuing systems, more recently, systems with no...

Since Age of Information (AoI) has been proposed as a metric that quantifies the freshness of information updates in a communication system, there has been a constant effort in understanding and optimizing different statistics of the AoI process for classical queueing systems. In addition to classical queuing systems, more recently, systems with no...

The main motivation behind extending the notion of Age-of-Information (AoI) to Networked Control Systems (NCS) stems from recent results suggesting that AoI can be used to reformulate some of the traditional NCS problems with a new perspective. In fact, lower AoI in a NCS results in lower estimation/control cost. This is intuitive as to improve est...

In their seminal work [8], Lenstra, Shmoys, and Tardos proposed a 2-approximation algorithm to solve the problem of scheduling jobs on unrelated parallel machines with the objective of minimizing makespan. In contrast to their model, where a job is processed to completion by scheduling it on any one machine, we consider the scenario where each job...

In this article, we investigate the transient behavior of a sequence of packets/bits traversing a multi-hop wireless network. Our work is motivated by novel applications from the domain of process automation, Machine-Type Communication (MTC) and cyber-physical systems, where short messages are communicated and statistical guarantees need to be prov...

Age of Information (AoI) has proven to be a useful metric in networked systems where timely information updates are of importance. Recently, minimizing the "average age" has received considerable attention. However, various applications pose stricter age requirements on the updates which demand knowledge of the AoI distribution. In this work, we st...

Since IEEE has standardized 802.11 protocol for WLANs [1], significant work has been done in developing rate adaptation algorithms. Most of the rate adaptation algorithms proposed till now are heuristic, sub-optimal and are competitive in nature. Even though these algorithms have advantage of implementing in distributed fashion, their throughput pe...

We revisit the problem of assigning n jobs to m machines/servers. We study this problem under more general settings, which capture important aspects of applications that arise in networking and information systems. In particular, we consider jobs that have placement constraints and machines that are heterogeneous. The cost incurred at a machine is...

We study the scheduling of computational tasks on one local processor and one remote processor with communication delay. This problem has important application in cloud computing. Although the communication time to transmit a task can be inferred from the known data size of the task and the transmission bandwidth, the processing time of the task is...

We consider the scenario where a mobile device requires assistance from nearby devices to forward its computational tasks to a cloud server. We incentivize cooperation by allowing helper devices to conserve computational energy by offloading their own tasks to the source device's cloud, as compensation for the communication energy lost during task...

Since IEEE has standardized 802.11 protocol for WLANs, significant work has been done in developing rate adaptation algorithms. Most of the rate adaptation algorithms proposed till now are heuristic, suboptimal and are competitive in nature. Even though these algorithms have advantage of implementing in distributed fashion, their throughput perform...