Prasant Mohapatra

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

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Publications (242)93.56 Total impact

  • [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.
    09/2014;
  • Yunze Zeng, Parth H. Pathak, Chao Xu, Prasant Mohapatra
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    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%.
    09/2014;
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    ABSTRACT: As modern datacenter networks (DCNs) grow to support hundreds of thousands of servers and beyond, managing network equipment -- such as routers, firewalls, and load balancers -- becomes increasingly complex. Network attributes such as IP address allocations and BGP neighbor relations are scattered among various network engineering groups, which makes troubleshooting the network a cumbersome task. In addition, network vendor diversity leads to an explosion of vendor-specific management systems or single-use automation scripts, limiting network scalability while increasing the time required to perform management tasks. In this article, the authors propose a unified network management system, Switch Manager (SWIM), to cope with the growth by standardizing the language for describing network attributes and unifying the interface for executing management actions on the network equipment.
    IEEE Internet Computing 07/2014; 18(4):30-36. · 2.04 Impact Factor
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    ABSTRACT: Crowd-Cache is a novel crowd-sourced content caching system which provides cheap and convenient content access for mobile users. Our system exploits both transient colocation of devices and the spatial temporal correlation of content popularity, where users in a particular location and at specific times would be likely interested in similar content. We demonstrate the feasibility of Crowd-Cache system through a prototype implementation on Android smartphones.
    06/2014;
  • Shaxun Chen, Amit Pande, Prasant Mohapatra
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    ABSTRACT: Facial recognition is a popular biometric authentica-tion technique, but it is rarely used in practice for de-vice unlock or website / app login in smartphones, alt-hough most of them are equipped with a front-facing camera. Security issues (e.g. 2D media attack and vir-tual camera attack) and ease of use are two important factors that impede the prevalence of facial authentica-tion in mobile devices. In this paper, we propose a new sensor-assisted facial authentication method to over-come these limitations. Our system uses motion and light sensors to defend against 2D media attacks and virtual camera attacks without the penalty of authenti-cation speed. We conduct experiments to validate our method. Results show 95-97% detection rate and 2-3% false alarm rate over 450 trials in real-settings, indicat-ing high security obtained by the scheme ten times faster than existing 3D facial authentications (3 sec-onds compared to 30 seconds).
    06/2014;
  • Yunze Zeng, Parth H. Pathak, Prasant Mohapatra
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    ABSTRACT: This paper is first of its kind in presenting a detailed characterization of IEEE 802.11ac using real experiments. 802.11ac is the latest WLAN standard that is rapidly being adapted due to 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 QAM) and increased number of spatial streams for MIMO. We provide an experiment evaluation of these factors and their impact using a 18-nodes 802.11ac testbed. Our findings provide numerous insights on benefits and challenges associated with using 802.11ac in practice. Since utilization 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 utilizing larger channel width is in general less energy efficient due to 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.
    2014 IFIP Networking Conference; 06/2014
  • Debalina Ghosh, Prasant Mohapatra
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    ABSTRACT: Femtocells offer many advantages in wireless networks such as improved cell capacity and coverage in indoor areas. As these femtocells can be deployed in an ad-hoc manner by different consumers in the same frequency band, the femtocells can interfere with each other. To fully realize the potential of the femtocells, it is necessary to allocate resources to them in such a way that interference is mitigated. We propose a distributed resource allocation algorithm for femtocell networks that is modelled after link-state routing protocols. Resource Allocation using Link State Propagation (RALP) consists of a graph formation stage, where individual femtocells build a view of the network, an allocation stage, where every femtocell executes an algorithm to assign OFDMA resources to all the femtocells in the network and local scheduling stage, where a femtocell assigns resources to all user equipments based on their throughput requirements. Our evaluation shows that RALP performs better than existing femtocell resource allocation algorithms with respect to spatial reuse and satisfaction rate of required throughput.
    Computer Communications 06/2014; · 1.08 Impact Factor
  • Source
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    ABSTRACT: The rise of location-based services has enabled many opportunities for content service providers to optimize the content delivery based on user's location. Since sharing precise location remains a major privacy concern among the users, many location-based services rely on contextual location (e.g. residence, cafe etc.) as opposed to acquiring user's exact physical location. In this paper, we present PACL (Privacy-Aware Contextual Localizer), which can learn user's contextual location just by passively monitoring user's network traffic. PACL can discern a set of vital attributes (statistical and application-based) from user's network traffic, and predict user's contextual location with a very high accuracy. We design and evaluate PACL using real-world network traces of over 1700 users with over 100 gigabytes of total data. Our results show that PACL (built using decision tree) can predict user's contextual location with the accuracy of around 87%.
    IEEE INFOCOM 2014, Toronto; 04/2014
  • ACM/IEEE Conference on Information Processing in Sensor Networks; 04/2014
  • Amit Pande, Shaxun Chen, Prasant Mohapatra, Gaurav Pande
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    ABSTRACT: Blocking is a common artifact in wireless video streaming services, mainly attributed to packet loss degradation in real-time transmission scenarios. In this paper, we present a simple algorithm and architecture for robust detection of blocking artifact. We first take Discrete Wavelet Transform of the original video frame followed by utilizing a unique property of staircase sized repeated pattern in the videos. The variance of this pattern is measured as the extent of blocking in a video frame. We propose two architectures for blocking detection: one using orthogonal wavelets which can be seamlessly integrated to video source authentication, and the other based on bi-orthogonal wavelets, and can be used in robust stand-alone blocking detection. A prototype implementation on a Xilinx Virtex-6 XC6VLX75 FPGA device was optimized to obtain a clock frequency of 167 (396) MHz or orthogonal (and bi-orthogonal wavelets) using 4 (0) multipliers in the design respectively.
    Proceedings of the 2014 27th International Conference on VLSI Design and 2014 13th International Conference on Embedded Systems; 01/2014
  • Amit Pande, Shaxun Chen, Prasant Mohapatra, Joseph Zambreno
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    ABSTRACT: Digital camera identification can be accomplished based on sensor pattern noise, which is unique to a device, and serves as a distinct identification fingerprint. Camera identification and authentication have formed the basis of image/video forensics in legal proceedings. Unfortunately, real-time video source identification is a computationally heavy task, and does not scale well to conventional software implementations on typical embedded devices. In this paper, we propose a hardware architecture for source identification in networked cameras. The underlying algorithms, an orthogonal forward and inverse discrete wavelet transform and minimum mean square error-based estimation, have been optimized for 2-D frame sequences in terms of area and throughput performance. We exploit parallelism, pipelining, and hardware reuse techniques to minimize hardware resource utilization and increase the achievable throughput of the design. A prototype implementation on a Xilinx Virtex-6 FPGA device was optimized with a resulting throughput of 167 MB/s, processing 30 640 × 480 video frames in 0.17 s.
    IEEE Transactions on Circuits and Systems for Video Technology 01/2014; 24(1):157-167. · 1.82 Impact Factor
  • Source
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    ABSTRACT: Adhoc networks enable communication between distributed, mobile wireless nodes without any supporting infrastructure. In the absence of centralized control, such networks require node interaction, and are inherently based on cooperation between nodes. In this paper, we use social and behavioral trust of nodes to form a flow allocation optimization problem. We initialize trust using information gained from users' social relationships (from social networks) and update the trusts metric over time based on observed node behaviors. We conduct analysis of social trust using real data sets and used it as a parameter for performance evaluation of our frame work in ns-3. Based on our approach we obtain a significant improvement in both detection rate and packet delivery ratio using social trust information when compared to behavioral trust alone. Further, we observe that social trust is critical in the event of mobility and plays a crucial role in bootstrapping the computation of trust.
    11/2013;
  • Source
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    ABSTRACT: Accurate and online Energy Expenditure Estimation (EEE) utilizing small wearable sensors is a difficult task with most existing schemes. In this work, we focus on accurate EEE for tracking ambulatory activities of a common smartphone user. We used existing smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately detect EEE. Using Artificial Neural Networks, a machine learning technique, a generic regression model for EEE is built that yields upto 83% correlation with actual Energy Expenditure (EE). Using barometer data, in addition to accelerometry is found to significantly improve EEE performance (upto 10%). We compare our results against state-of-the-art Calorimetry Equations (CE) and consumer electronics devices (Fitbit and Nike+ Fuel Band).
    Proceedings of the 4th Conference on Wireless Health; 11/2013
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    ABSTRACT: Smartphones and WiFi networks are becoming pervasive. As a result, new applications and services are being offered to smartphone users through WiFi networks. Some of the more novel applications provide data services to pedestrians as they move through WiFi coverage areas in public loca- tions such as railway stations. One significant factor that will influence the data transfers for users when they are on the move, is the connection set-up time. In this paper we characterize the WiFi connection set-up process. Using data from voluntary Android smartphone users, we show that WiFi connection setup have significant delays, sometimes as high as 10s. Then through a detailed analysis of the con- nection set-up process we show that, contrary to previous findings, this is due to losses of DHCP messages at the WiFi access point. We also show that some of the methods that have been adopted by device manufactures are suboptimal and this can be addressed at the WiFi access point. Finally using this insight we extend a known mathematical model, which will help in the dimensioning of WiFi networks for pedestrian smartphone users.
    Proceedings of the 8th ACM international workshop on Wireless network testbeds, experimental evaluation & characterization; 09/2013
  • Victor Omwando, Amit Pande, Yunze Zeng, Prasant Mohapatra
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    ABSTRACT: In this paper, we characterize the performance of HD video streaming in 802.11n WLANs under user mobility. We conducted experiments in QuRiNet, a large-scale outdoor wireless testbed that experiences little electromagnetic interference. We observe the variation in video quality with the variance of both speed of a mobile user and his distance from access point (AP). Using subjective scores and objective video quality assessment metrics, we build a non-linear regression model to estimate video quality based on user speed and distance. An ensemble machine learning kernel, bagging, is used in conjunction with Reduced Error Pruning Decision Trees to build a non-linear prediction model that scores 69% correlation with video quality. Overall, we find that distance has larger impact on video quality than speed. However, the physical factors such as speed and distance cannot be used in isolation to estimate video quality accurately.
    Proceedings of the 8th ACM international workshop on Wireless network testbeds, experimental evaluation & characterization; 09/2013
  • Source
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    ABSTRACT: Energy Expenditure Estimation (EEE) is an important step in tracking personal activity and preventing chronic diseases such as obesity, diabetes and cardiovascular diseases. Accurate and online EEE utilizing small wearable sensors is a difficult task, primarily because most existing schemes work offline or using heuristics. In this work, we focus on accurate EEE for tracking ambulatory activities (walking, standing, climbing upstairs or downstairs) of a common smartphone user. We used existing smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately detect EEE. Using Artificial Neural Networks, a machine learning technique, we build a generic regression model for EEE that yields upto 89% correlation with actual Energy Expenditure (EE). Using barometer data, in addition to accelerometry is found to significantly improve EEE performance (upto 15%). We compare our results against state-of-the-art Calorimetry Equations (CE) and consumer electronics devices (Fitbit and Nike+ Fuel Band). We were able to demonstrate the superior accuracy achieved by our algorithm. The results were calibrated against COSMED K4b2 calorimeter readings.
    Proceedings of the 8th International Conference on Body Area Networks; 09/2013
  • Amit Pande, Eilwoo Baik, Prasant Mohapatra
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    ABSTRACT: There is an increase rise in the usage of mobile health sensors in wearable devices and smartphones. These embedded systems have tight limits on storage, computation power, network connectivity and battery usage making it important to ensure efficient storage/ communication of sensor readings to centralized node/ server. Frequency Transform or Entropy encoding schemes such as arithmetic or Huffman coding can be used for compression, but they incur high computational cost in some scenarios or are oblivious to the higher level redundancies in signal. To this end, we used the property of periodicity in these naturally occurring signals such as heart rate or gait measurements to design a simple low cost scheme for data compression. First, a modified Chi-square periodogram metric is used to adaptively determine the exact time-varying periodicity of the signal. Next, the time-series signal is folded into Frames of length equal to a pre-determined period value. We have successfully tested the scheme for good compression performance in ECG, motion accelerometer data and Parkinson patients samples, leading to 8-14X compression in large sample sizes (6-8K samples) and 2-3X in small sample sizes (200 samples). The proposed scheme can be used stand-alone or as pre-processing step for existing techniques in literature.
    Proceedings of the 3rd ACM MobiHoc workshop on Pervasive wireless healthcare; 07/2013
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    ABSTRACT: Mobile in-app ads are the major funding source for free mobile apps which users download from various app markets and install in their smartphones. However, a number researchers have recently pointed out that ad-supported free apps involve hidden costs to the users. Costs are primarily associated with the loss of privacy due to personal data collection, using data from the mobile data quota and draining of smartphone battery. The alternative proposals for the problem do not address all of these issues. We propose a novel ads delivery architecture, which addresses all these issues, by extending the users' personal domain to the cloud through personal cloudlets. Personal cloudlets preserve user privacy by only releasing high level user information to the ad network, reduce energy consumption by performing the computations, communications and storage in the "cloud", and minimize the bandwidth usage by passing only selected information between personal cloudlet and the smartphone. Furthermore, the proposed architecture, provides a number of incentives for ad networks to use the architecture.
    Proceedings of the first international workshop on Mobile cloud computing & networking; 07/2013
  • Source
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    ABSTRACT: The Domain Name System (DNS) provides a critical service for the Internet -- mapping of user-friendly domain names to their respective IP addresses. Yet, there is no standard set of metrics quantifying the Quality of Domain Name Service (QoDNS), let alone a thorough evaluation of it. This article attempts to fill this gap from the perspective of a DNS proxy/cache, which is the bridge between clients and authoritative servers. We present an analytical model of DNS proxy operations that offers insights into the design trade-offs of DNS infrastructure and the selection of critical DNS parameters. Due to the critical role DNS proxies play in QoDNS, they are the focus of attacks including cache poisoning attack. We extend the analytical model to study DNS cache poisoning attacks and their impact on QoDNS metrics. This analytical study prompts us to present Domain Name Cross-Referencing (DoX), a peer-to-peer systems for DNS proxies to cooperatively defend cache poisoning attacks. Based on QoDNS, we compare DoX with the cryptography-based DNS Security Extension (DNSSEC) to understand their relative merits.
    ACM Transactions on Internet Technology (TOIT). 05/2013; 12(3).
  • Shaxun Chen, Kai Zeng, Prasant Mohapatra
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    ABSTRACT: In cognitive radio networks, an attacker transmits signals mimicking the characteristics of primary signals, in order to prevent secondary users from transmitting. Such an attack is called primary user emulation (PUE) attack. TV towers and wireless microphones are two main types of primary users in white space. Existing work on PUE attack detection only focused on the first category. For the latter category, primary users are mobile and their transmission power is low. These properties introduce great challenges on PUE detection and existing methods are not applicable. In this paper, we propose a novel method to detect the emulation attack of wireless microphones. We exploit the relationship between RF signals and acoustic information to verify the existence of wireless microphones. The effectiveness of our approach is validated through real-world implementation. Extensive experiments show that our method achieves both false positive rate and false negative rate lower than 0.1 even in a noisy environment.
    IEEE Transactions on Mobile Computing 03/2013; 12(3):401-411. · 2.40 Impact Factor

Publication Stats

3k Citations
93.56 Total Impact Points

Institutions

  • 1970–2014
    • University of California, Davis
      • Department of Computer Science
      Davis, California, United States
  • 2012–2013
    • University of Michigan-Dearborn
      • Department of Computer & Information Science
      Dearborn, Michigan, United States
    • University of Michigan
      Ann Arbor, Michigan, United States
  • 2011
    • Beijing Institute Of Technology
      Peping, Beijing, China
    • Nokia Research Center
      Palo Alto, California, United States
  • 2009
    • Carleton University
      • Department of Systems and Computer Engineering
      Ottawa, Ontario, Canada
    • Technische Universiteit Delft
      • Faculty of Electrical Engineering, Mathematics and Computer Sciences (EEMCS)
      Delft, South Holland, Netherlands
  • 2007
    • CSU Mentor
      Long Beach, California, United States
  • 2004
    • Intel
      Santa Clara, California, United States
    • Ball State University
      Muncie, Indiana, United States
  • 2002
    • EMC Corporation
      Hopkinton, Massachusetts, United States
    • Advanced Micro Devices
      Sunnyvale, California, United States
  • 2000–2001
    • Michigan State University
      • Department of Computer Science and Engineering
      East Lansing, Michigan, United States
  • 1999
    • Iowa State University
      • Department of Electrical and Computer Engineering
      Ames, IA, United States