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Prasenjit Karmakar

Prasenjit Karmakar appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Machine LearningPerformanceArtificial IntelligenceDistributed, Parallel, and Cluster ComputingHuman-Computer Interactionmath.PR

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Published work

6 published item(s)

preprint2026arXiv

PoHAR: Understanding Hyperlocal Human Activities with Pollution Sensor Networks

Low-cost air quality sensors are becoming ubiquitous in our daily lives as public awareness of air pollution continues to grow, and people take measures to monitor and improve the air they breathe indoors. Besides the standard operation of these sensors, fluctuations in environmental parameters can be leveraged to understand human behavior and activities in indoor spaces. Unlike traditional audio-visual, Radio Frequency, and inertial sensors, air quality sensors are easily scalable to a household, are privacy-preserving, and more economical. Such distributed sensor networks must jointly make decisions to monitor indoor occupants for downstream smart home and healthcare applications. However, due to low processing power, memory, and energy, they often struggle to maintain distributed data consensus and identify activity-affected sensor groups for accurate on-device inference. In this paper, we propose PoHAR framework that implements: (i) a conflict-free replicated data primitive for data sharing, (ii) a hierarchical clustering for ESP32 to detect activity-affected sensor groups with a self-supervised distance metric, and (iii) a leader-based group inference with off-the-shelf ML classifiers, enabling the sensor network to collaboratively detect hyperlocal indoor activities. Our extensive experiments demonstrated on-device activity detection, achieving 97.41% accuracy for indoor activity and 99.68% for cooking activity, using off-the-shelf ML models with latency below 34 microseconds.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Exploiting Multi-modal Contextual Sensing for City-bus's Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction

Intelligent city transportation systems are one of the core infrastructures of a smart city. The true ingenuity of such an infrastructure lies in providing the commuters with real-time information about citywide transports like public buses, allowing her to pre-plan the travel. However, providing prior information for transportation systems like public buses in real-time is inherently challenging because of the diverse nature of different stay-locations that a public bus stops. Although straightforward factors stay duration, extracted from unimodal sources like GPS, at these locations look erratic, a thorough analysis of public bus GPS trails for 720km of bus travels at the city of Durgapur, a semi-urban city in India, reveals that several other fine-grained contextual features can characterize these locations accurately. Accordingly, we develop BuStop, a system for extracting and characterizing the stay locations from multi-modal sensing using commuters' smartphones. Using this multi-modal information BuStop extracts a set of granular contextual features that allow the system to differentiate among the different stay-location types. A thorough analysis of BuStop using the collected dataset indicates that the system works with high accuracy in identifying different stay locations like regular bus stops, random ad-hoc stops, stops due to traffic congestion stops at traffic signals, and stops at sharp turns. Additionally, we also develop a proof-of-concept setup on top of BuStop to analyze the potential of the framework in predicting expected arrival time, a critical piece of information required to pre-plan travel, at any given bus stop. Subsequent analysis of the PoC framework, through simulation over the test dataset, shows that characterizing the stay-locations indeed helps make more accurate arrival time predictions with deviations less than 60s from the ground-truth arrival time.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Customer-server population dynamics in heavy traffic

We study a many-server queueing model with server vacations, where the population size dynamics of servers and customers are coupled: a server may leave for vacation only when no customers await, and the capacity available to customers is directly affected by the number of servers on vacation. We focus on scaling regimes in which server dynamics and queue dynamics fluctuate at matching time scales, so that their limiting dynamics are coupled. Specifically, we argue that interesting coupled dynamics occur in (a) the Halfin-Whitt regime, (b) the nondegenerate slowdown regime, and (c) the intermediate, near Halfin-Whitt regime; whereas the dynamics asymptotically decouple in the other heavy traffic regimes. We characterize the limiting dynamics, which are different for each scaling regime. We consider relevant respective performance measures for regimes (a) and (b) --- namely, the probability of wait and the slowdown. While closed form formulas for these performance measures have been derived for models that do not accommodate server vacations, it is difficult to obtain closed form formulas for these performance measures in the setting with server vacations. Instead, we propose formulas that approximate these performance measures, and depend on the steady-state mean number of available servers and previously derived formulas for models without server vacations. We test the accuracy of these formulas numerically.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

On a convergent off -policy temporal difference learning algorithm in on-line learning environment

In this paper we provide a rigorous convergence analysis of a "off"-policy temporal difference learning algorithm with linear function approximation and per time-step linear computational complexity in "online" learning environment. The algorithm considered here is TDC with importance weighting introduced by Maei et al. We support our theoretical results by providing suitable empirical results for standard off-policy counterexamples.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Are Markov Models Effective for Storage Reliability Modelling?

Continuous Time Markov Chains (CTMC) have been used extensively to model reliability of storage systems. While the exponentially distributed sojourn time of Markov models is widely known to be unrealistic (and it is necessary to consider Weibull-type models for components such as disks), recent work has also highlighted some additional infirmities with the CTMC model, such as the ability to handle repair times. Due to the memoryless property of these models, any failure or repair of one component resets the "clock" to zero with any partial repair or aging in some other subsystem forgotten. It has therefore been argued that simulation is the only accurate technique available for modelling the reliability of a storage system with multiple components. We show how both the above problematic aspects can be handled when we consider a careful set of approximations in a detailed model of the system. A detailed model has many states, and the transitions between them and the current state captures the "memory" of the various components. We model a non-exponential distribution using a sum of exponential distributions, along with the use of a CTMC solver in a probabilistic model checking tool that has support for reducing large state spaces. Furthermore, it is possible to get results close to what is obtained through simulation and at much lower cost.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Scalable Reliability Modelling of RAID Storage Subsystems

Reliability modelling of RAID storage systems with its various components such as RAID controllers, enclosures, expanders, interconnects and disks is important from a storage system designer's point of view. A model that can express all the failure characteristics of the whole RAID storage system can be used to evaluate design choices, perform cost reliability trade-offs and conduct sensitivity analyses. However, including such details makes the computational models of reliability quickly infeasible. We present a CTMC reliability model for RAID storage systems that scales to much larger systems than heretofore reported and we try to model all the components as accurately as possible. We use several state-space reduction techniques at the user level, such as aggregating all in-series components and hierarchical decomposition, to reduce the size of our model. To automate computation of reliability, we use the PRISM model checker as a CTMC solver where appropriate. Our modelling techniques using PRISM are more practical (in both time and effort) compared to previously reported Monte-Carlo simulation techniques. Our model for RAID storage systems (that includes, for example, disks, expanders, enclosures) uses Weibull distributions for disks and, where appropriate, correlated failure modes for disks, while we use exponential distributions with independent failure modes for all other components. To use the CTMC solver, we approximate the Weibull distribution for a disk using sum of exponentials and we confirm that this model gives results that are in reasonably good agreement with those from the sequential Monte Carlo simulation methods for RAID disk subsystems reported in literature earlier. Using a combination of scalable techniques, we are able to model and compute reliability for fairly large configurations with upto 600 disks using this model.

0 citations0 reviewsNo code linkedOpen access