Prasant Mohapatra

University of California, Davis, Davis, California, United States

Are you Prasant Mohapatra?

Claim your profile

Publications (330)192.63 Total impact

  • Xinlei Wang · Amit Pande · Jindan Zhu · Prasant Mohapatra
    [Show abstract] [Hide abstract]
    ABSTRACT: Location-based services are quickly becoming immensely popular. In addition to services based on users' current location, many potential services rely on users' location history, or their spatial-temporal provenance. Malicious users may lie about their spatial-temporal provenance without a carefully designed security system for users to prove their past locations. In this paper, we present the Spatial-Temporal provenance Assurance with Mutual Proofs (STAMP) scheme. STAMP is designed for ad-hoc mobile users generating location proofs for each other in a distributed setting. However, it can easily accommodate trusted mobile users and wireless access points. STAMP ensures the integrity and non-transferability of the location proofs and protects users' privacy. A semi-trusted Certification Authority is used to distribute cryptographic keys as well as guard users against collusion by a light-weight entropy-based trust evaluation approach. Our prototype implementation on the Android platform shows that STAMP is low-cost in terms of computational and storage resources. Extensive simulation experiments show that our entropy-based trust model is able to achieve high ( > 0.9) collusion detection accuracy.
    No preview · Article · Jan 2016 · IEEE/ACM Transactions on Networking
  • Source
    Dataset: PMC

    Full-text · Dataset · Dec 2015
  • Source

    Full-text · Article · Oct 2015 · Pervasive and Mobile Computing
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: It is reported that mobile users spend most of their time on texting SMS, Social Networking, Emailing, or sending instant messaging (IM), all of which involve text input. There are three primary text input modalities, soft keyboard (SK), speech to text (STT) and Swype. Each one of them engages a different set of hardware and consequently consumes different amounts of battery energy. Using high-precision power measurement hardware and systematically taking into account the user context, we characterize and compare the energy consumption of these three input modalities. We find that the length of interaction, or the message length, determines the most energy efficient modality. For short interactions, less than 14-30 characters, SK is the most energy efficient. For longer interactions, however, STT significantly outperforms both SK and Swype. When message length distributions of popular text activities are considered, STT provides near optimal energy consumption without requiring the user to predict the message length and decide between SK and STT. In terms of battery life, the choice of input modality makes significant differences. If users always choose SK for all their text activities, they will consume nearly 50% of the phone battery each day. Choosing STT over SK can save 30%-40% of the battery depending on the choice of STT software.
    Full-text · Article · Oct 2015
  • Eilwoo Baik · Amit Pande · Prasant Mohapatra
    [Show abstract] [Hide abstract]
    ABSTRACT: Wireless communication systems are highly prone to channel errors. With video being a major player in Internet traffic and undergoing exponential growth in wireless domain, we argue for the need of a Video-aware MAC (VMAC) to significantly improve the throughput and delay performance of real-time video streaming service. VMAC makes two changes to optimize wireless LAN for video traffic: (a) It incorporates a Perceptual-Error-Tolerance (PET) to the MAC frames by reducing MAC retransmissions while minimizing any impact on perceptual video quality; and (b) It uses a group NACK-based Adaptive Window (NAW) of MAC frames to improve both throughput and delay performance in varying channel conditions. Through simulations and experiments, we observe 56-89% improvement in throughput and 34-48% improvement in delay performance over legacy DCF and 802.11e schemes. VMAC also shows 15-78% improvement over legacy schemes with multiple clients.
    No preview · Article · Jun 2015 · ACM Transactions on Multimedia Computing Communications and Applications
  • Prasant Mohapatra
    [Show abstract] [Hide abstract]
    ABSTRACT: This installment of Computer's series highlighting the work published in IEEE Computer Society journals comes from IEEE Transactions on Mobile Computing.
    No preview · Article · Jun 2015 · Computer
  • Source
    S. Seneviratne · A. Seneviratne · A. Mahanti · P. Mohapatra

    Full-text · Conference Paper · May 2015
  • Yunze Zeng · Parth H. Pathak · Prasant Mohapatra
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper is the first of its kind in presenting a detailed characterisation of IEEE 802.11ac using real experiments. 802.11ac is the latest Wireless Local Area Network (WLAN) standard that is rapidly being adapted because of its potential to deliver very high throughput. The throughput increase in 802.11ac can be attributed to three factors—larger channel width (80/160 MHz), support for denser modulation (256 Quadrature Amplitude Modulation (QAM)) and increased number of spatial streams for Multiple-input Multiple-output (MIMO). We provide an experiment evaluation of these factors and their impact using a real 802.11ac testbed. Our findings provide numerous insights on benefits and challenges associated with using 802.11ac in practice.Because utilisation of larger channel width is one of the most significant changes in 802.11ac, we focus our study on understanding its impact on energy efficiency and interference. Using experiments, we show that utilising larger channel width is in general less energy efficient because of its higher power consumption in idle listening mode. Increasing the number of MIMO spatial streams is comparatively more energy efficient for achieving the same percentage increase in throughput. We also show that 802.11ac link witnesses severe unfairness issues when it coexists with legacy 802.11. We provide a detailed analysis to show how medium access in heterogeneous channel width environment leads to the unfairness issues. We believe that these and many other findings presented in this work will help in understanding and resolving various performance issues of next generation WLANs. Copyright © 2015 John Wiley & Sons, Ltd.
    No preview · Article · May 2015 · Transactions on Emerging Telecommunications Technologies
  • Source
    F Jiang · E Zarepour · M Hassan · A Seneviratne · P Mohapatra

    Full-text · Conference Paper · Mar 2015
  • Parth H. Pathak · Rudra Dutta · Prasant Mohapatra
    [Show abstract] [Hide abstract]
    ABSTRACT: It is understood from past decade of research that a wireless multi-hop network can achieve maximum network throughput only when its nodes operate at a minimum common transmission power level that ensures network connectivity (availability). This point of optimality where maximum availability and throughput is guaranteed in an interference-optimal network has been the basis of numerous design problems in wireless networks. In this paper, we claim that when performability (availability weighted performance) is considered as opposed to average case throughput performance, there does not exist a transmission power (or node density) that can maximize both availability and performability. Since the current mesh networks are expected to deliver carrier-grade services to its users, the availability-performability tradeoff presented in this paper holds a special importance. While availability metric is a necessary one for any networking system intended to provide continuous service, past research has shown a strong correlation between performability and quality of user experience in case of wireless networks. The contributions of the paper are as follows: (1) We first define availability and performability in the context of wireless mesh networks, and then develop efficient algorithms on the basis of intelligent state sampling that can calculate both the quantities with reasonable accuracy. (2) We apply the evaluation methods to two existing mesh networks (GoogleWiFi and PoncaCityMesh) to demonstrate that their current design can not guarantee a reasonable level of availability or performability. (3) Using hundreds of hours of simulations, we analyze the impact of two basic deployment factors (node density and transmission power) on availability and performability. We outline numerous novel results that emerge due to joint availability-performability analysis including the observation about availability-performability tradeoff.
    No preview · Article · Mar 2015 · IEEE Transactions on Mobile Computing
  • [Show abstract] [Hide abstract]
    ABSTRACT: Accurate Energy Expenditure (EE) Estimation is very important to monitor physical activity of healthy and disabled population. In this work, we examine the limitations of applying existing calorimetry equations and machine learning models based on sensor data collected from healthy adults to estimate EE in disabled population, particularly children with Duchene muscular dystrophy (DMD). We propose a new machine learning-based approach which provides more accurate EE estimation for boys living with DMD. Existing calorimetry equations obtain a correlation of 40% (93% relative error in linear regression) with COSMED indirect calorimeter readings, while the non-linear model derived for normal healthy adults (developed using machine learning) gave 37% correlation. The proposed model for boys with DMD give a 91% correlation with COSMED values (only 38% relative absolute error) and uses ensemble meta-classifier with Reduced Error Pruning Decision Trees methodology.
    No preview · Article · Feb 2015
  • Jeongho Kwak · Okyoung Choi · Song Chong · Prasant Mohapatra

    No preview · Article · Jan 2015 · IEEE/ACM Transactions on Networking
  • Chen Lyu · Dawu Gu · Yunze Zeng · Prasant Mohapatra

    No preview · Article · Jan 2015 · IEEE Transactions on Dependable and Secure Computing
  • Amit Pande · Jindan Zhu · Aveek Das · Yunze Zeng · Prasant Mohapatra · Jay Han
    [Show abstract] [Hide abstract]
    ABSTRACT: Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. Accurate and real-time EE estimation utilizing small wearable sensors is a difficult task, primarily because the most existing schemes work offline or use heuristics. In this paper, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs, or downstairs) of a typical smartphone user. We used built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate EE. Using a barometer sensor, in addition to an accelerometer sensor, greatly increases the accuracy of EE estimation. Using bagged regression trees, a machine learning technique, we developed a generic regression model for EE estimation that yields upto 96% correlation with actual EE. We compare our results against the state-of-the-art calorimetry equations and consumer electronics devices (Fitbit and Nike+ FuelBand). The newly developed EE estimation algorithm demonstrated superior accuracy compared with currently available methods. The results were calibrated against COSMED K4b2 calorimeter readings.
    No preview · Article · Jan 2015 · IEEE Journal of Translational Engineering in Health and Medicine
  • Rong Jin · Liu Shi · Kai Zeng · Amit Pande · Prasant Mohapatra
    [Show abstract] [Hide abstract]
    ABSTRACT: With the prevalence of mobile computing, lots of wireless devices need to establish secure communication on the fly without pre-shared secrets. Device pairing is critical for bootstrapping secure communication between two previously unassociated devices over the wireless channel. Using auxiliary out-of-band channels involving visual, acoustic, tactile or vibrational sensors has been proposed as a feasible option to facilitate device pairing. However, these methods usually require users to perform additional tasks such as copying, comparing, and shaking. It is preferable to have a natural and intuitive pairing method with minimal user tasks. In this paper, we introduce a new method, called MagPairing, for pairing smartphones in close proximity by exploiting correlated magnetometer readings. In MagPairing, users only need to naturally tap the smartphones together for a few seconds without performing any additional operations in authentication and key establishment. Our method exploits the fact that smartphones are equipped with tiny magnets. Highly correlated magnetic field patterns are produced when two smartphones are close to each other. We design MagPairing protocol and implement it on Android smartphones. We conduct extensive simulation and experiments to evaluate MagPairing. Experimental results show that MagPairing can successfully pair two smartphones with 4.5 seconds on average. It is immune to man-in-the-middle attack even when the attacker is a few centimeters away from the pairing devices.
    No preview · Article · Jan 2015 · IEEE Transactions on Information Forensics and Security
  • Wei Cheng · Jindan Zhu · Prasant Mohapatra · Jie Wang
    [Show abstract] [Hide abstract]
    ABSTRACT: Time-critical Location Based Service (LBS) applications in mobile ad hoc networks require fast localization. The conventional localization techniques are, unfortunately, unsuitable for such applications, for they neglect the time needed for localization. As a result, time-critical information may become obsolete, and the mobile users such as vehicles may have moved to new locations before the localization procedure is completed. To address this issue, we formulate a notion of On-Demand Fast Localization (ODFL) and devise a framework to implement this concept over existing routing protocols in MANETs. We present analytical and simulation results to demonstrate that ODFL can significantly reduce the time solely needed for localization before starting time-critical applications. Moreover, we show that ODFL can also improve location privacy and reduce energy consumptions.
    No preview · Article · Dec 2014
  • Xinlei Oscar Wang · Wei Cheng · Prasant Mohapatra · Tarek Abdelzaher
    [Show abstract] [Hide abstract]
    ABSTRACT: Mobile sensing is becoming a popular paradigm to collect information from and outsource tasks to mobile users. These applications deal with lot of personal information, e.g., identity and location. Therefore, we need to pay a deeper attention to privacy and anonymity. However, the knowledge of the data source is desired to evaluate the trustworthiness of the sensing data. Anonymity and trust become two conflicting objectives in mobile sensing. In this paper, we propose ARTSense, a framework to solve the problem of “trust without identity” in mobile sensing. Our solution consists of a privacy-preserving provenance model, a data trust assessment scheme and an anonymous reputation management protocol. In contrast to other recent solutions, our scheme does not require a trusted third party and both positive and negative reputation updates can be enforced. In the trust assessment, we consider contextual factors that dynamically affects the trustworthiness of the sensing data as well as the mutual support and conflict among data from difference sources. Security analysis shows that ARTSense achieves our desired anonymity and security goals. Our prototype implementation on Android demonstrates that ARTSense incurs minimal computation overhead on mobile devices, and simulation results justify that ARTSense captures the trust of information and reputation of participants accurately.
    No preview · Article · Dec 2014 · IEEE Transactions on Mobile Computing
  • [Show abstract] [Hide abstract]
    ABSTRACT: Recently, there have been proposals to evade censors by using steganography to embed secret messages in images shared on public photo-sharing sites. However, establishing a covert channel in this manner is not straightforward. First, photo-sharing sites often process uploaded images, thus destroying any embedded message. Second, prior work assumes the existence of an out-of-band channel, using which the communicating users can exchange metadata or secret keys a priori; establishing such out-of-band channels, not monitored by censors, is difficult. In this paper, we address these issues to facilitate private communications on photo-sharing sites. In doing so, first, we conduct an in-depth measurement study of the feasibility of hiding data on four popular photo-sharing sites. Second, based on the understanding derived, we propose a novel approach for embedding secret messages in uploaded photos while preserving the integrity of such messages. We demonstrate that, despite the processing on photo-sharing sites, our approach ensures reliable covert communication, without increasing the likelihood of being detected via steganalysis. Lastly, we design and implement a scheme for bootstrapping private communications without an out-of-band channel, i.e., by exchanging keys via uploaded images.
    No preview · Conference Paper · Oct 2014
  • Yunze Zeng · Parth H. Pathak · Chao Xu · Prasant Mohapatra
    [Show abstract] [Hide abstract]
    ABSTRACT: Recent WiFi standards use Channel State Information (CSI) feedback for better MIMO and rate adaptation. CSI provides detailed information about current channel conditions for different subcarriers and spatial streams. In this paper, we show that CSI feedback from a client to the AP can be used to recognize different fine-grained motions of the client. We find that CSI can not only identify if the client is in motion or not, but also classify different types of motions. To this end, we propose APsense, a framework that uses CSI to estimate the sensor patterns of the client. It is observed that client's sensor (e.g. accelerometer) values are correlated to CSI values available at the AP. We show that using simple machine learning classifiers, APsense can classify different motions with accuracy as high as 90%.
    No preview · Article · Sep 2014
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we highlight a potential privacy threat in the current smartphone platforms, which allows any third party to collect a snapshot of installed applications without the user's consent. This can be exploited by third parties to infer various user attributes similar to what is done through tracking. We show that using only installed apps, user's gender, a demographic attribute that is frequently used in targeted advertising, can be instantly predicted with an accuracy around 70%, by training a classifier using established supervised learning techniques.
    Full-text · Article · Sep 2014 · ACM SIGMOBILE Mobile Computing and Communications Review

Publication Stats

5k Citations
192.63 Total Impact Points


  • 1970-2015
    • University of California, Davis
      • Department of Computer Science
      Davis, California, United States
  • 2011
    • University of California, Riverside
      • Department of Electrical Engineering
      Riverside, California, United States
  • 2009
    • Technische Universiteit Delft
      • Faculty of Electrical Engineering, Mathematics and Computer Sciences (EEMCS)
      Delft, South Holland, Netherlands
  • 1994-2008
    • Iowa State University
      • Department of Electrical and Computer Engineering
      Ames, Iowa, United States
  • 2002
    • Advanced Micro Devices
      Sunnyvale, California, United States
  • 1999-2001
    • Michigan State University
      • Department of Computer Science and Engineering
      East Lansing, Michigan, United States
  • 1995
    • Pennsylvania State University
      • Department of Computer Science and Engineering
      University Park, Maryland, United States