Showing papers by "Samir R. Das published in 2019"

Advancing User Quality of Experience in 360-degree Video Streaming

Abstract: Conventional streaming solutions for streaming 360- degree panoramic videos are inefficient in that they download the entire 360-degree panoramic scene, while the user views only a small sub-part of the scene called the viewport. This can waste over 80% of the network bandwidth. We develop a comprehensive approach called Mosaic that combines a powerful neural network-based viewport prediction with a rate control mechanism that assigns rates to different tiles in the 360-degree frame such that the video quality of experience is optimized subject to a given network capacity. We model the optimization as a multi-choice knapsack problem and solve it using a greedy approach.We also develop an end-to-end testbed using standardscompliant components and provide a comprehensive performance evaluation of Mosaic along with four other streaming techniques – two for conventional adaptive video streaming and two for 360- degree tile-based video streaming. Mosaic outperforms the best of the competition by as much as 50% in terms of median video quality.

Reading detection in real-time

Abstract: Observable reading behavior, the act of moving the eyes over lines of text, is highly stereotyped among the users of a language, and this has led to the development of reading detectors-methods that input windows of sequential fixations and output predictions of the fixation behavior during those windows being reading or skimming. The present study introduces a new method for reading detection using Region Ranking SVM (RRSVM). An SVM-based classifier learns the local oculomotor features that are important for real-time reading detection while it is optimizing for the global reading/skimming classification, making it unnecessary to hand-label local fixation windows for model training. This RRSVM reading detector was trained and evaluated using eye movement data collected in a laboratory context, where participants viewed modified web news articles and had to either read them carefully for comprehension or skim them quickly for the selection of keywords (separate groups). Ground truth labels were known at the global level (the instructed reading or skimming task), and obtained at the local level in a separate rating task. The RRSVM reading detector accurately predicted 82.5% of the global (article-level) reading/skimming behavior, with accuracy in predicting local window labels ranging from 72-95%, depending on how tuned the RRSVM was for local and global weights. With this RRSVM reading detector, a method now exists for near real-time reading detection without the need for hand-labeling of local fixation windows. With real-time reading detection capability comes the potential for applications ranging from education and training to intelligent interfaces that learn what a user is likely to know based on previous detection of their reading behavior.

MMLite: A Scalable and Resource Efficient Control Plane for Next Generation Cellular Packet Core

Abstract: With increase in cellular-enabled IoT devices having diverse traffic characteristics and service level objectives (SLOs), handling the control traffic in a scalable and resource-efficient manner in the cellular packet core network is critical. The traditional monolithic design of the cellular core adopted by service-providers is inflexible with respect to the diverse requirements and bursty loads of IoT devices, specifically for properties such as elasticity, customizability, and scalability. To address this key challenge, we focus on the most critical control plane component of the cellular packet core network, the Mobility Management Entity (MME). We present MMLite, a functionally decomposed and stateless MME design wherein individual control procedures are implemented as microservices and states are decoupled from their processing, thus enabling elasticity and fault tolerance. For SLO compliance, we develop a multi-level load balancing approach based on skewed consistent hashing to efficiently distribute incoming connections. We evaluate the performance benefits of MMLite over existing approaches with respect to scaling, fault tolerance, SLO compliance and resource efficiency.

ProCSA: Protecting Privacy in Crowdsourced Spectrum Allocation

Abstract: Sharing a spectrum is an emerging paradigm to increase spectrum utilization and thus address the unabated increase in mobile data consumption. The paradigm allows the “unused” spectrum bands of licensed primary users to be shared with secondary users, as long as the allocated spectrum to the secondary users does not cause any harmful interference to the primary users. However, such shared spectrum paradigms pose serious privacy risks to the participating entities, e.g., the secondary users may be sensitive about their locations and usage patterns. This paper presents a privacy-preserving protocol for the shared spectrum allocation problem in a crowdsourced architecture, wherein spectrum allocation to secondary users is done based on real-time sensing reports from geographically distributed and crowdsourced spectrum sensors. Such an architecture is highly desirable since it obviates the need to assume a propagation model, and facilitates estimation based on real-time propagation conditions and high granularity data via inexpensive means.

Passive Wireless Channel Estimation in RF Tag Network

Abstract: We envision a future where every object in our living and working environment will carry one or more RF tags. Based on the backscattering tag-to-tag communication link, these RF tags will be connected in a network without the need for the central interrogating device. We present a novel tag architecture that enables estimation of the parameters of wireless tag-to-tag channel by a passive receiver. Sampling the received baseband signal at different reflecting phases at the backscattering tag enables estimation of amplitude and phase of the tag-to-tag channel. The low-power implementation of the channel estimator, after envelope detection, integrates amplification and filtering of the baseband signal that is followed by analog-to-digital conversion. The channel estimator, implemented in 65 nm CMOS technology, has sensitivity of −45 dBm at 2.5% modulation index and consumes 122 nW.

Collaborative Channel Estimation in Backscattering Tag-to-Tag Networks

Abstract: Backscattering Tag-to-Tag Networking (BTTN) represents a rapidly emerging paradigm enabling passive, radio-less tags to communicate directly with each other by reflecting (backscattering) an RF signal supplied by an external un-coordinated exciter. Recent advancements have taken the capability of BTTN beyond basic communication, empowering the networks with the ability to collaboratively sense and recognize human activities in the deployment space. The key to this ability is a novel passive channel estimation, allowing individual tags to measure tag-to-tag wireless channel parameters without involvement of any active radio. Previously reported techniques for this suffer from a limitation in that, they are unable to isolate the tag-to-tag channel of interest from the wider-range exciter-to-tag channels. As a result, the channel estimates and the analytics based thereof are susceptible to dynamic variations and clutter in the overall deployment environment, outside the range of the tag-to-tag link. In this paper, we overcome these limitations using a novel collaborative technique thus greatly enhancing the utility of passive channel estimation in BTTN. We elucidate our proposed technique using analytical modeling and validate with in-lab experiments using tag hardware built from discrete components.

VISCR: Intuitive & Conflict-free Automation for Securing the Dynamic Consumer IoT Infrastructures.

Abstract: Consumer IoT is characterized by heterogeneous devices with diverse functionality and programming interfaces. This lack of homogeneity makes the integration and security management of IoT infrastructures a daunting task for users and administrators. In this paper, we introduce VISCR, a Vendor-Independent policy Specification and Conflict Resolution engine that enables conflict-free policy specification and enforcement in IoT environments. VISCR converts the topology of the IoT infrastructure into a tree-based abstraction and translates existing policies from heterogeneous vendor-specific programming languages such as Groovy-based SmartThings, OpenHAB, IFTTT-based templates, and MUD-based profiles into a vendor-independent graph-based specification. Using the two, VISCR can automatically detect rouge policies, conflicts, and bugs for coherent automation. Upon detection, VISCR infers new policies and proposes them to users as alternatives to existing policies for fine-tuning and conflict-free enforcement. We evaluated VISCR using a dataset of 907 IoT apps, programmed using heterogeneous automation specifications in a simulated smart-building IoT infrastructure. In our experiments, among 907 IoT apps, VISCR exposed 342 of IoT apps as exhibiting one or more violations. VISCR detected 100% of violations reported by existing state-of-the-art tool, while detecting new types of violations in an additional 266 apps. In terms of performance, VISCR can generate 400 abstraction trees (used in specifying policies) with 100K leaf nodes in <1.2sec. In our experiments, VISCR took 80.7 seconds to analyze our infrastructure of 907 apps; a 14.2X reduction compared to the state-of-the-art. After the initial analysis, VISCR is capable of adopting new policies in sub-second latency to handle changes.

Spectrum Protection from Micro-transmissions Using Distributed Spectrum Patrolling

Abstract: RF spectrum is a limited natural resource under a significant demand and thus must be effectively monitored and protected. Recently, there has been a significant interest in the use of inexpensive commodity-grade spectrum sensors for large-scale RF spectrum monitoring. The spectrum sensors are attached to compute devices for signal processing computation and also network and storage support. However, these compute devices have limited computation power that impacts the sensing performance adversely. Thus, the parameter choices for the best performance must be done carefully taking the hardware limitations into account. In this paper, we demonstrate this using a benchmarking study, where we consider the detection an unauthorized transmitter that transmits intermittently only for very small durations (micro-transmissions). We characterize the impact of device hardware and critical sensing parameters such as sampling rate, integration size and frequency resolution in detecting such transmissions. We find that in our setup we cannot detect more than 45% of such micro-transmissions on these inexpensive spectrum sensors even with the best possible parameter setting. We explore use of multiple sensors and sensor fusion as an effective means to counter this problem.

Coordinated dataflow protection for ultra-high bandwidth science networks

Abstract: The Science DMZ (SDMZ) is a special purpose network architecture proposed by ESnet (Energy Sciences Network) to facilitate distributed science experimentation on terabyte- (or petabyte-) scale data, exchanged over ultra-high bandwidth WAN links. Critical security challenges faced by these networks include: (i) network monitoring at high bandwidths, (ii) reconciling site-specific policies with project-level policies for conflict-free policy enforcement, (iii) dealing with geographically-distributed datasets with varying levels of sensitivity, and (iv) dynamically enforcing appropriate security rules. To address these challenges, we develop a fine-grained dataflow-based security enforcement system, called CoordiNetZ (CNZ), that provides coordinated situational awareness, i.e., the use of context-aware tagging for policy enforcement using the dynamic contextual information derived from hosts and network elements. We also developed tag and IP-based security microservices that incur minimal overheads in enforcing security to data flows exchanged across geographically-distributed SDMZ sites. We evaluate our prototype implementation across two geographically distributed SDMZ sites with SDN-based case studies, and present performance measurements that respectively highlight the utility of our framework and demonstrate efficient implementation of security policies across distributed SDMZ networks.

Study of mm-sized Coil to Coil Backscatter Based Communication Link

Abstract: Distribution of a large number of mm-sized sensing units in brain is a vision for the next generation of implantable devices for neural recording. Recorded data from the implants is conventionally transferred to a central external device and the bandwidth of the uplink channel is limited by the number of the implanted units. For the first time, we demonstrate the feasibility of local communication between mm-sized coils using backscattering technique which promises to reduce the requirement on the uplink bandwidth between the external device and the implants. To demonstrate the feasibility of the proposed link, two implanted coils located at 14 mm implantation depth are used with the distance between coils of 1.5 mm. The transmitting coil switches between two terminating impedance and the input voltage at the receiving coil is observed in the simulations with a triple-loop inductive link designed at 90 MHz. We show that the voltage difference in the received signal for two transmitting states can be resolved by demodulator of the receiving implant demonstrating the link feasibility. Several simulations show the functionality of the link under wide range of different angular and lateral misalignment.

RF-based Analytics Generated by Tag-to-tag Networks

Abstract: We have developed a type of RFID tags that can communicate with each other directly if there is an RF signal in their environment to support backscattering. These tags are passive and they can form a tag-to-tag network. Our tags communicate by what we refer to as multiphase probing. With this technique, we basically explore the backscatter channel by reflecting the incident RF signal with different changes in the phase. We define a measure of the backscatter channel, which we call backscatter channel state information (BCSI). The BCSI is composed of backscatter channel phase, backscatter amplitude, and change in baseline excitation level. When acquired over time, this measure provides rich RF analytics that can be used to extract various types of information from the environment of the tags by signal processing/machine learning methods. We show in the paper that this analytics is invariant w.r.t. to some variables including the deployment environment. We provide results from experiments with our tags that demonstrate the invariance of the BCSI.

Creating Spatio-temporal Spectrum Maps from Sparse Crowdsensed Data

Abstract: Shared spectrum systems is an emerging paradigm to improve spectrum utilization and thus address the unabated increase in mobile data consumption. The paradigm allows the “unused” spectrum bands of licensed Primary Users (PUs) to be shared with Secondary Users (SUs), without causing any harmful interference to the PUs. Allocation of spectrum to the SUs is done based on spectrum availability at the SUs' locations; such allocation of spectrum is greatly facilitated by spectrum occupancy maps. In this work, we address the problem of creating spectrum occupancy maps from spectrum occupancy data over a large number of instants, in the challenging scenario of dynamically (temporally) changing spectrum occupancy due to intermittent transmission of primary users. The problem is particularly challenging when the available occupancy data is very sparse spatially, i.e., only very few locations report sensing data at any particular instant. We design various techniques to create spectrum maps in the above context, including a promising correlation-based merging method that merges observation vectors iteratively in conjunction with careful interpolation. Using extensive simulation over data including real data from cellular and deployed WiFi settings, we show that the correlation-based method is very effective in generating high-accuracy spatiotemporal spectrum maps even with very sparse observation vectors (as long as the number of such vectors is large enough).

Handcrafted vs Deep Learning Classification for Scalable Video QoE Modeling

Abstract: Mobile video traffic is dominant in cellular and enterprise wireless networks. With the advent of diverse applications, network administrators face the challenge to provide high QoE in the face of diverse wireless conditions and application contents. Yet, state-of-the-art networks lack analytics for QoE, as this requires support from the application or user feedback. While there are existing techniques to map QoS to QoE by training machine learning models without requiring user feedback, these techniques are limited to only few applications, due to insufficient QoE ground-truth annotation for ML. To address these limitations, we focus on video telephony applications and model key artefacts of spatial and temporal video QoE. Our key contribution is designing content- and device-independent metrics and training across diverse WiFi conditions. We show that our metrics achieve a median 90% accuracy by comparing with mean-opinion-score from more than 200 users and 800 video samples over three popular video telephony applications -- Skype, FaceTime and Google Hangouts. We further extend our metrics by using deep neural networks, more specifically we use a combined CNN and LSTM model. We achieve a median accuracy of 95% by combining our QoE metrics with the deep learning model, which is a 38% improvement over the state-of-the-art well known techniques.