arXiv subjects

Rohit Singh

Publications and source records attributed to Rohit Singh.

At least 37 records · Page 2Linked to original sources

arXiv2021

Consenting to Internet of Things Across Different Social Settings

Devices connected to the Internet of Things (IoT) are rapidly becoming ubiquitous across modern homes, workplaces, and other social environments. While these devices provide users with extensive functionality, they pose significant privacy concerns due to difficulties in consenting to these devices. In this work, we present the results of a pilot study that shows how users consent to devices in common locations at a friends house in which the user is a guest attending a party. We use this pilot study to indicate a direction for a larger study, which will capture a more granular understanding of how users will consent to a variety of devices placed in different social settings (i.e. a party house owned by a friend, an office space for the user and some 40 other employees, the bathroom of a department store). Our final contribution of this work will be to build a probability distribution which will indicate how probable a given user is to consent to a device given what sensors it has, where it is, and the awareness and preferences of each user.

cs.CR↗

arXiv2020

Ultra-dense Low Data Rate (UDLD) Communication in the THz

In the future, with the advent of Internet of Things (IoT), wireless sensors, and multiple 5G killer applications, an indoor room might be filled with $1000$s of devices demanding low data rates. Such high-level densification and mobility of these devices will overwhelm the system and result in higher interference, frequent outages, and lower coverage. The THz band has a massive amount of greenfield spectrum to cater to this dense-indoor deployment. However, a limited coverage range of the THz will require networks to have more infrastructure and depend on non-line-of-sight (NLOS) type communication. This form of communication might not be profitable for network operators and can even result in inefficient resource utilization for devices demanding low data rates. Using distributed device-to-device (D2D) communication in the THz, we can cater to these Ultra-dense Low Data Rate (UDLD) type applications. D2D in THz can be challenging, but with opportunistic allocation and smart learning algorithms, these challenges can be mitigated. We propose a 2-Layered distributed D2D model, where devices use coordinated multi-agent reinforcement learning (MARL) to maximize efficiency and user coverage for dense-indoor deployment. We show that densification and mobility in a network can be used to further the limited coverage range of THz devices, without the need for extra infrastructure or resources.

cs.NI↗

arXiv2020

SHINE (Strategies for High-frequency INdoor Environments) with Efficient THz-AP Placement

The increasing demand for ultra-high throughput in ultra-dense networks might take a toll on 5G capacity. Moreover, with Internet-of-Things (IoT) and the growing use-cases for indoor killer-applications, it will be necessary to look beyond 5G technologies. One such promising technology is to move higher in the frequencies, such as the THz ($300$ $GHz$-$10$ $THz$) spectrum. THz has a massive number of greenfield-contiguous channels ranging from $10$ $GHz$ to $200$ $GHz$ (best case), which was not available in the traditional radio frequency (RF) or millimeter wave (mmWave) bands. Although THz has immense potential to cater to such demands, it comes with numerous challenges revolving around hardware, link budget, mobility, blockages, scheduling, and deployment. Terahertz access points (THz-APs) are sensitive to deployment and can critically impact a system\textquotesingle s dynamics (i.e., coverage, throughput, and efficiency). In this paper, we present Strategies for High-frequency INdoor Environments or SHINE, which focuses on efficient AP deployment in the THz spectrum and draws motivation from approaches used indoor to improve lighting conditions. Due to THz\textquotesingle s limited coverage area of a few meters, the number of THz-APs required to satisfy a densely populated room will be higher compared to today's single router/box/AP model. This increased number of THz-APs will not only increase the operational costs, but also (in some cases) can make the system inefficient. Through SHINE, we explore the deployment-related challenges and propose strategies to mitigate the same.

cs.NI↗

arXiv2020

Green Security Game with Community Engagement

While game-theoretic models and algorithms have been developed to combat illegal activities, such as poaching and over-fishing, in green security domains, none of the existing work considers the crucial aspect of community engagement: community members are recruited by law enforcement as informants and can provide valuable tips, e.g., the location of ongoing illegal activities, to assist patrols. We fill this gap and (i) introduce a novel two-stage security game model for community engagement, with a bipartite graph representing the informant-attacker social network and a level-$κ$ response model for attackers inspired by cognitive hierarchy; (ii) provide complexity results and exact, approximate, and heuristic algorithms for selecting informants and allocating patrollers against level-$κ$ ($κ<\infty$) attackers; (iii) provide a novel algorithm to find the optimal defender strategy against level-$\infty$ attackers, which converts the problem of optimizing a parameterized fixed-point to a bi-level optimization problem, where the inner level is just a linear program, and the outer level has only a linear number of variables and a single linear constraint. We also evaluate the algorithms through extensive experiments.

cs.GT↗

arXiv2020

An Analytical Model for Efficient Indoor THz Access Point Deployment

Ultra-densification of user equipment (UE) and access points (APs) are anticipated to take a toll on the future spectrum needs. Higher frequency bands, such as mmWave ($30$-$300GHz$) and THz spectrum ($0.3$-$10THz$), can be used to cater to the high-throughput needs of ultra-dense networks. These high-frequency bands have a tremendous amount of \textit{green-filed contiguous spectrum}, ranging in hundreds of $GHz$. However, these bands, especially the THz bands, face numerous challenges, such as high spreading, absorption, and penetration losses. To combat these challenges, the THz-APs need to be either equipped with high transmit power, high antenna gains (i.e., narrow antenna beams), or limit the communication to short-ranges. All of these factors are bounded due to technical or economic challenges, which will result in a \textit{"distance-power dilemma"} while deciding on the deployment strategy of THz-APs. In this paper, we present an analytical model to deploy THz-APs in an indoor setting efficiently. We further show through extensive numerical analysis, the optimal number of APs and optimal room length for different blocks of the THz spectrum. Furthermore, these THz-APs need to be efficiently packed to avoid outages due to handoffs, which can add more complexity to the dilemma. To mitigate the packing problem, we propose two solutions over the optimal solution: (a) Radius Increase, and (b) Repeater Assistance, and present an analytical model for each.

cs.NI↗

arXiv2019

MOTH- Mobility-induced Outages in THz: A Beyond 5G (B5G) application

5G will enable the growing demand for Internet of Things (IoT), high-resolution video streaming, and low latency wireless services. Demand for such services is expected to growth rapid, which will require a search for Beyond 5G technological advancements in wireless communications. Part of these advancements is the need for additional spectrum, namely moving toward the terahertz (THz) range. To compensate for the high path loss in THz, narrow beamwidths are used to improve antenna gains. However, with narrow beamwidths, even minor fluctuations in device location (such as through body movement) can cause frequent link failures due to beam misalignment. In this paper, we provide a solution to these small-scale indoor movement that result in mobility-induced outages. Like a moth randomly flutters about, Mobility-induced Outages in THz (MOTH) can be ephemeral in nature and hard to avoid. To deal with MOTH we propose two methods to predict these outage scenarios: (i) Align-After-Failure (AAF), which predicts based on fixed time margins, and (ii) Align-Before-Failure (ABF), which learns the time margins through user mobility patterns. In this paper, two different online classifiers were used to train the ABF model to predicate if a mobility-induced outage is going to occur; thereby, significantly reducing the time spent in outage scenarios. Simulation results demonstrate a relationship between optimal beamwidth and human mobility patterns. Additionally, to cater to a future with dense deployment of Wireless Personal Area Network (WPAN), it is necessary that we have efficient deployment of resources (e.g., THz-APs). One solution is to maximize the user coverage for a single AP, which might be dependent on multiple parameters. We identify these parameters and observe their tradeoffs for improving user coverage through a single THz-AP.

cs.NI↗

arXiv2019

Reliable THz Communications for Outdoor based Applications- Use Cases and Methods

Future (beyond 5G) wireless networks will demand high throughput and low latency and would benefit from greenfield, contiguous, and wider bandwidth, all of which THz spectrum can provide. Although THz has been envisioned to be deployed in an indoor setting, with proper enforcement and planning, we can draw a limited number of use cases for outdoor THz communication. THz can provide high capacity and ultra-high throughput but at the cost of high path loss and sensitivity to device orientation/mobility.. We identify scenarios where the use of the THz spectrum for an outdoor setting is justified and their critical operating parameters. We further categorize the applications based on the relative mobility between the access point (AP) and user equipment (UE). We present an approach for deploying THz on an outdoor framework by presenting preliminary technical parameter analysis for scenarios, like wireless backhaul, high-speed kiosks, and the aerial base station (ABS). Our preliminary analysis shows that the application for each of these scenarios is limited based on multiple parameters, such as distance, device mobility, device orientation, user geometry, antenna gain, and environment settings, which requires separate consideration and optimization.

cs.NI↗

arXiv2019

Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations

Illegal wildlife poaching threatens ecosystems and drives endangered species toward extinction. However, efforts for wildlife protection are constrained by the limited resources of law enforcement agencies. To help combat poaching, the Protection Assistant for Wildlife Security (PAWS) is a machine learning pipeline that has been developed as a data-driven approach to identify areas at high risk of poaching throughout protected areas and compute optimal patrol routes. In this paper, we take an end-to-end approach to the data-to-deployment pipeline for anti-poaching. In doing so, we address challenges including extreme class imbalance (up to 1:200), bias, and uncertainty in wildlife poaching data to enhance PAWS, and we apply our methodology to three national parks with diverse characteristics. (i) We use Gaussian processes to quantify predictive uncertainty, which we exploit to improve robustness of our prescribed patrols and increase detection of snares by an average of 30%. We evaluate our approach on real-world historical poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia. (ii) We present the results of large-scale field tests conducted in Murchison Falls and Srepok Wildlife Sanctuary which confirm that the predictive power of PAWS extends promisingly to multiple parks. This paper is part of an effort to expand PAWS to 800 parks around the world through integration with SMART conservation software.

stat.AP↗

arXiv2019

Parameter Modeling for Small-Scale Mobility in Indoor THz Communication

Despite such challenges as high path loss and equipment cost, THz communication is becoming one of the potentially viable means through which ultra-high data rate can be achieved. To compensate for the high path loss, we present parameter modeling for indoor THz communication. To maximize efficient and opportunistic use of resources, we analyze the potential workarounds for a single access point to satisfy most of the mobile terminals by varying such parameters as humidity, distance, frequency windows, beamwidths, antenna placement, and user mobility type. One promising parameter is antenna beamwidth, where narrower beams results in higher antenna gain. However, this can lead to "\textit{beamwidth dilemma}" scenario, where narrower beamwidth can result in significant outages due to device mobility and orientation. In this paper, we address this challenge by presenting a mobility model that performs an extensive analysis of different human mobility scenarios, where each scenario has different data rate demands and movement patterns. We observe that for mobile users, there are optimal beamwidths that are affected by the mobility type (high mobility, constrained mobility, and low mobility) and AP placement.

cs.NI↗

arXiv2019

Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference

A regression-based BNN model is proposed to predict spatiotemporal quantities like hourly rider demand with calibrated uncertainties. The main contributions of this paper are (i) A feed-forward deterministic neural network (DetNN) architecture that predicts cyclical time series data with sensitivity to anomalous forecasting events; (ii) A Bayesian framework applying SVGD to train large neural networks for such tasks, capable of producing time series predictions as well as measures of uncertainty surrounding the predictions. Experiments show that the proposed BNN reduces average estimation error by 10% across 8 U.S. cities compared to a fine-tuned multilayer perceptron (MLP), and 4% better than the same network architecture trained without SVGD.

cs.LG↗

arXiv2018

Deep Reinforcement Learning for Green Security Games with Real-Time Information

Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing. However, real-time information such as footprints and agents' subsequent actions upon receiving the information, e.g., rangers following the footprints to chase the poacher, have been neglected in previous work. To fill the gap, we first propose a new game model GSG-I which augments GSGs with sequential movement and the vital element of real-time information. Second, we design a novel deep reinforcement learning-based algorithm, DeDOL, to compute a patrolling strategy that adapts to the real-time information against a best-responding attacker. DeDOL is built upon the double oracle framework and the policy-space response oracle, solving a restricted game and iteratively adding best response strategies to it through training deep Q-networks. Exploring the game structure, DeDOL uses domain-specific heuristic strategies as initial strategies and constructs several local modes for efficient and parallelized training. To our knowledge, this is the first attempt to use Deep Q-Learning for security games.

cs.MA↗

arXiv2018

Pyro: Deep Universal Probabilistic Programming

Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large datasets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs.

cs.LG↗

arXiv2018

Designing the Game to Play: Optimizing Payoff Structure in Security Games

Effective game-theoretic modeling of defender-attacker behavior is becoming increasingly important. In many domains, the defender functions not only as a player but also the designer of the game's payoff structure. We study Stackelberg Security Games where the defender, in addition to allocating defensive resources to protect targets from the attacker, can strategically manipulate the attacker's payoff under budget constraints in weighted L^p-norm form regarding the amount of change. Focusing on problems with weighted L^1-norm form constraint, we present (i) a mixed integer linear program-based algorithm with approximation guarantee; (ii) a branch-and-bound based algorithm with improved efficiency achieved by effective pruning; (iii) a polynomial time approximation scheme for a special but practical class of problems. In addition, we show that problems under budget constraints in L^0-norm form and weighted L^\infty-norm form can be solved in polynomial time. We provide an extensive experimental evaluation of our proposed algorithms.

cs.GT↗

arXiv2016

Reply to Comment on Nonlocal quartic interactions and universality classes in perovskite manganites

Comment [arXiv:cond-mat.stat.mech., 1602.02087v1 (2016)] has raised questions claiming that the nonlocal model Hamiltonian presented in [Phys. Rev. E 92, 012123 (2015)] is equivalent to the standard (short-ranged) Φ^4 theory. These claims are based on a low momentum expansion of the interaction vertex that cannot be applied to the vertex factors containing both low and high momenta inside the loop-integrals. Elaborating upon the important steps of the momentum shell decimation scheme, employed in the renormalization-group calculation, we explicitly show the interplay of internal (high) and external (low) momenta determining the loop integrals for self-energy and vertex functions giving rise to corrections (to the bare parameters) different from those of the standard (short-ranged) Φ^4 theory. Employing explicit mathematical arguments, we show that this difference persists when the range of interaction is assumed to be long (short) ranged with respect to the lattice constant (correlation-length), yielding the critical exponents as given in the original paper.

cond-mat.stat-mech↗

arXiv2016

Automatic Generation of Formula Simplifiers based on Conditional Rewrite Rules

This paper addresses the problem of creating simplifiers for logic formulas based on conditional term rewriting. In particular, the paper focuses on a program synthesis application where formula simplifications have been shown to have a significant impact. We show that by combining machine learning techniques with constraint-based synthesis, it is possible to synthesize a formula simplifier fully automatically from a corpus of representative problems, making it possible to create formula simplifiers tailored to specific problem domains. We demonstrate the benefits of our approach for synthesis benchmarks from the SyGuS competition and automated grading.

cs.PL↗

arXiv2015

A Tunable Terahertz Detector Based On Self Assembled Plasmonic Structure on a GaAs 2DEG

Plasmonic detectors have the potential to provide a method of rapid spectroscopy without the need of moving mirrors or gratings. Previous measurements have demonstrated frequency tunable detection based on plasmonic excitations, however these devices were either small area, polarization dependent and/or required e-beam lithography. We demonstrate that large area high sensitivity THz plasmonic detection can be achieved using self-assembly nanosphere lithography. We achieve a submicron feature size grid covering a detector area of 4 mm^2. The absorbance at the fundamental, third and 5th harmonics of plasmon resonances are observed. Measurements at 80 K show a large transmission change of 25%. We also confirmed a magneto plasmon dispersion of this device. In this paper we will discuss the frequency dependence of the radiative damping effect which affects enhanced absorption at the higher harmonics mode relative to fundamental.

cond-mat.mtrl-sci↗

arXiv2013

Understanding the metamagnetic transition and magnetic behavior of Ni48Co6Mn26Al20 polycrystalline ribbons

In this work we demonstrate that the polycrystalline ribbons of (Ni48Co6)Mn26Al20 with B2 structure at room temperature show a magnetic behavior with competing magnetic exchange interactions leading to frozen disorders at low temperatures. It is established that by considering the presence of both antiferromagnetic and ferromagnetic sublattices, we can explain the observed magnetic behavior including the metamagnetic transition observed in these samples. From the Arrott plots, the Néel temperature of (Ni48Co6)Mn26Al20 is deduced to be ~170 K and the broad ferro to para like magnetic phase transition is observed at ~ 200 K. Based on Néel theory, a cluster model is used to explain the presence of ferromagnetic and anti-ferromagnetic clusters in the studied ribbons. Formation of ferromagnetic clusters can be understood in terms of positive exchange interactions among the Mn atoms that are neighboring to Co atoms which are located on the Ni sites.

cond-mat.mtrl-sci↗

arXiv2013

Data Hiding in Binary Image using Block Parity

Secret data hiding in binary images is more difficult than other formats since binary images require only one bit representation to indicate black and white. This study proposes a new method for data hiding in binary images using optimized bit position to replace a secret bit. This method manipulates blocks, which are sub-divided. The parity bit for a specified block decides whether to change or not, to embed a secret bit. By finding the best position to insert a secret bit for each divided block, the image quality of the resulting stego-image can be improved, while maintaining low computational complexity.The experimental results show that the proposed method has an improvement with respect to a previous work.

cs.CR↗