Prasant Mohapatra

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

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Publications (306)185.47 Total impact

  • Parth H. Pathak, Rudra Dutta, Prasant Mohapatra
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    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.
    IEEE Transactions on Mobile Computing 03/2015; 14(3):606-618. DOI:10.1109/TMC.2014.2329845 · 2.91 Impact Factor
  • Chen Lyu, Dawu Gu, Yunze Zeng, Prasant Mohapatra
    IEEE Transactions on Dependable and Secure Computing 01/2015; DOI:10.1109/TDSC.2015.2399297 · 1.14 Impact Factor
  • 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%.
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    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.
    ACM SIGMOBILE Mobile Computing and Communications Review 09/2014; 18(3). DOI:10.1145/2646584.2646587
  • Source
    Prasant Mohapatra
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    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. DOI:10.1109/MC.2014.240 · 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. DOI:10.1109/MIC.2014.41 · 2.00 Impact Factor
  • Shraboni Jana, Eilwoo Baik, Amit Pande, Prasant Mohapatra
    IEEE SECON 2014; 06/2014
  • ACM SIGMOBILE Mobile Computing and Communications Review 06/2014; 18(2):1-8. DOI:10.1145/2636242.2636244
  • 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).
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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.
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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. DOI:10.1109/TNET.2013.2270436 · 1.99 Impact Factor
  • 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; 46. DOI:10.1016/j.comcom.2014.03.007 · 1.35 Impact Factor
  • 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
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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
  • Jeongho Kwak, Okyoung Choi, Song Chong, Prasant Mohapatra
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    ABSTRACT: Energy-delay tradeoffs in smartphone applications have been studied independently in dynamic voltage and frequency scaling (DVFS) problem and network interface selection problem. We optimize the two problems jointly to quantify how much energy can be saved further and propose a scheme called SpeedControl which jointly manages application scheduling, CPU speed control and wireless interface selection. The scheme is shown to be near-optimal in that it tends to minimize energy consumption for given delay constraints. This paper is the first to reveal energy-delay tradeoffs in a holistic view considering multiple wireless interfaces, DVFS and multitasking in smartphone. We perform real measurements on WiFi/3G coverage and throughput, power consumption of CPU and WiFi/3G interfaces, and CPU workloads. Trace-driven simulations based on the measurements demonstrate that SpeedControl can save over 30% of battery by trading 10 min delay as compared to existing schemes when WiFi temporal coverage is 65%, moreover, the saving tendency increases as WiFi coverage increases.
    IEEE INFOCOM 2014 - IEEE Conference on Computer Communications; 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. DOI:10.1109/TCSVT.2013.2276869 · 2.26 Impact Factor
  • Xinlei Wang, Wei Cheng, Prasant Mohapatra, Tarek Abdelzaher
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    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.
    IEEE Transactions on Mobile Computing 01/2014; 13(12):2777-2790. DOI:10.1109/TMC.2013.150 · 2.91 Impact Factor
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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.

Publication Stats

4k Citations
185.47 Total Impact Points

Institutions

  • 1970–2014
    • 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
    • EMC Corporation
      Hopkinton, Massachusetts, 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