Prasant Mohapatra

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

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Publications (253)159.01 Total impact

  • Parth H. Pathak, Rudra Dutta, Prasant Mohapatra
    IEEE Transactions on Mobile Computing 03/2015; 14(3):606-618. · 2.91 Impact Factor
  • Chen Lyu, Dawu Gu, Yunze Zeng, Prasant Mohapatra
    IEEE Transactions on Dependable and Secure Computing 01/2015; · 1.14 Impact Factor
  • [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;
  • Source
    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.
    Computer 09/2014; 47(9):6-6. · 1.44 Impact Factor
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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. · 2.00 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; · 1.35 Impact Factor
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    ABSTRACT: The Ethernet switch is a primary building block for today's enterprise networks and data centers. As network technologies converge upon a single Ethernet fabric, there is ongoing pressure to improve the performance and efficiency of the switch while maintaining flexibility and a rich set of packet processing features. The OpenFlow architecture aims to provide flexibility and programmable packet processing to meet these converging needs. Of the many ways to create an OpenFlow switch, a popular choice is to make heavy use of ternary content addressable memories (TCAMs). Unfortunately, TCAMs can consume a considerable amount of power and, when used to match flows in an OpenFlow switch, put a bound on switch latency. In this paper, we propose enhancing an OpenFlow Ethernet switch with per-port packet prediction circuitry in order to simultaneously reduce latency and power consumption without sacrificing rich policy-based forwarding enabled by the OpenFlow architecture. Packet prediction exploits the temporal locality in network communications to predict the flow classification of incoming packets. When predictions are correct, latency can be reduced, and significant power savings can be achieved from bypassing the full lookup process. Simulation studies using actual network traces indicate that correct prediction rates of 97% are achievable using only a small amount of prediction circuitry per port. These studies also show that prediction circuitry can help reduce the power consumed by a lookup process that includes a TCAM by 92% and simultaneously reduce the latency of a cut-through switch by 66%.
    IEEE/ACM Transactions on Networking 06/2014; 22(3):1007-1020. · 1.99 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. · 2.26 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;
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    ABSTRACT: While conventional cognitive radio (CR) system is striving at providing best possible protections for the usage of primary users (PU), little attention has been given to ensure the quality of service (QoS) of applications of secondary users (SU). When loading real-time applications over such a CR system, we have found that existing spectrum sensing schemes create a major hurdle for real-time traffic delivery of SU. For example, energy detection based sensing, a widely used technique, requires possibly more than 100 ms to detect a PU with weak signals. The delay is intolerable for real-time applications with stringent QoS requirements, such as voice over internet protocol (VoIP) or live video chat. This delay, along with other delays caused by backup channel searching, channel switching, and possible buffer overflow due to the insertion of sensing periods, makes supporting real-time applications over CR system very difficult if not impossible. In this paper, we present the design and implementation of a sensing-based CR system - RECOG, which is able to support realtime communications among SUs. We first redesign the conventional sensing scheme. Without increasing the complexity or trading off the detection performance, we break down a long sensing period into a series of shorter blocks, turning a disruptive long delay into negligible short delays. To enhance the sensing capability as well as better protect the QoS of SU traffic, we also incorporate an on-demand sensing scheme based on MAC layer information. In addition, to ensure a fast and reliable switching when PU returns, we integrate an efficient backup channel scanning and searching component in our system. Finally, to overcome a potential buffer overflow, we propose a CR-aware QoS manager. Our extensive experimental evaluations validate that RECOG can not only support realtime traffic among SUs with high quality, but also improve protections for PUs.
    IEEE Journal on Selected Areas in Communications 11/2013; 31(11):2504-2516. · 4.14 Impact Factor
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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
  • Source
    Amit Pande, Prasant Mohapatra, Joseph Zambreno
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    ABSTRACT: Algorithmic parameterization and hardware architectures can ensure secure transmission of multimedia data in resource-constrained environments such as wireless video surveillance networks, telemedicine frameworks for distant health care support in rural areas, and Internet video streaming. Joint multimedia compression and encryption techniques can significantly reduce the computational requirements of video processing systems. The authors present an approach to reduce the computational cost of multimedia encryption while also preserving the properties of compressed video. A hardware-amenable design of the proposed algorithms makes them suitable for real-time embedded multimedia systems. This approach alleviates the need for additional hardware for encryption in resource-constrained scenarios and can be otherwise used to augment existing encryption methods used for content delivery on the Internet or in other applications. This work shows how two compression blocks for video coding--a modified frequency transform (called a secure wavelet transform or SWT) and a modified entropy coding scheme (called a chaotic arithmetic coding or CAC)--can be used for video encryption. Experimental results are shown for selective encryption using the proposed schemes.
    IEEE Multimedia 10/2013; 20(4):50-61. · 1.77 Impact Factor
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    ABSTRACT: Received signal strength based fingerprinting approaches have been widely exploited for localization. The received signal strength (RSS) plays a very crucial role in determining the nature and characteristics of location fingerprints stored in a radio-map. The received signal strength is a function of distance between the transmitter and receiving device, which varies due to various in-path interferences. A detailed analysis of factors affecting the received signal for indoor localization is presented in this paper. The paper discusses the effect of factors such as spatial, temporal, environmental, hardware and human presence on the received signal strength through extensive measurements in a typical IEEE 802.11b/g/n network. It also presents the statistical analysis of the measured data that defines the reliability of RSS-based location fingerprints for indoor localization.
    2013 IEEE 38th Conference on Local Computer Networks (LCN 2013); 10/2013

Publication Stats

4k Citations
159.01 Total Impact Points

Institutions

  • 1970–2014
    • University of California, Davis
      • Department of Computer Science
      Davis, California, United States
  • 2012
    • University of Michigan-Dearborn
      • Department of Computer & Information Science
      Dearborn, 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
  • 2004
    • Intel
      Santa Clara, California, United States
  • 2002
    • EMC Corporation
      Hopkinton, Massachusetts, United States
    • Advanced Micro Devices
      Sunnyvale, California, United States
  • 2000–2002
    • Michigan State University
      • Department of Computer Science and Engineering
      East Lansing, MI, United States