Parul Pandey

THE BOSS EMISSION-LINE LENS SURVEY (BELLS). I. A LARGE SPECTROSCOPICALLY SELECTED SAMPLE OF LENS GALAXIES AT REDSHIFT ∼0.5*

We present a catalog of 25 definite and 11 probable strong galaxy–galaxy gravitational lens systems with lens redshifts 0.4 <~ z <~ 0.7, discovered spectroscopically by the presence of higher-redshift emission lines within the Baryon Oscillation Spectroscopic Survey (BOSS) of luminous galaxies, and confirmed with high-resolution Hubble Space Telescope (HST) images of 44 candidates. Our survey extends the methodology of the Sloan Lens Advanced Camera for Surveys survey (SLACS) to higher redshift.We describe the details of the BOSS spectroscopic candidate detections, our HST ACS image processing and analysis methods, and our strong gravitational lens modeling procedure. We report BOSS spectroscopic parameters and ACS photometric parameters for all candidates, and mass-distribution parameters for the best-fit singular isothermal ellipsoid models of definite lenses. Our sample to date was selected using only the first six months of BOSS survey-quality spectroscopic data. The full five-year BOSS database should produce a sample of several hundred strong galaxy–galaxy lenses and in combination with SLACS lenses at lower redshift, strongly constrain the redshift evolution of the structure of elliptical, bulgedominated galaxies as a function of luminosity, stellar mass, and rest-frame color, thereby providing a powerful test for competing theories of galaxy formation and evolution.

Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges

MEC is an emerging paradigm that provides computing, storage, and networking resources within the edge of the mobile RAN. MEC servers are deployed on a generic computing platform within the RAN, and allow for delay-sensitive and context-aware applications to be executed in close proximity to end users. This paradigm alleviates the backhaul and core network and is crucial for enabling low-latency, high-bandwidth, and agile mobile services. This article envisions a real-time, context-aware collaboration framework that lies at the edge of the RAN, comprising MEC servers and mobile devices, and amalgamates the heterogeneous resources at the edge. Specifically, we introduce and study three representative use cases ranging from mobile edge orchestration, collaborative caching and processing, and multi-layer interference cancellation. We demonstrate the promising benefits of the proposed approaches in facilitating the evolution to 5G networks. Finally, we discuss the key technical challenges and open research issues that need to be addressed in order to efficiently integrate MEC into the 5G ecosystem.

Collaborative multi-bitrate video caching and processing in Mobile-Edge Computing networks

Recently, Mobile-Edge Computing (MEC) has arisen as an emerging paradigm that extends cloud-computing capabilities to the edge of the Radio Access Network (RAN) by deploying MEC servers right at the Base Stations (BSs). In this paper, we envision a collaborative joint caching and processing strategy for on-demand video streaming in MEC networks. Our design aims at enhancing the widely used Adaptive BitRate (ABR) streaming technology, where multiple bitrate versions of a video can be delivered so as to adapt to the heterogeneity of user capabilities and the varying of network condition. The proposed strategy faces two main challenges: (i) not only the videos but their appropriate bitrate versions have to be effectively selected to store in the caches, and (ii) the transcoding relationships among different versions need to be taken into account to effectively utilize the processing capacity at the MEC servers. To this end, we formulate the collaborative joint caching and processing problem as an Integer Linear Program (ILP) that minimizes the backhaul network cost, subject to the cache storage and processing capacity constraints. Due to the NP-completeness of the problem and the impractical overheads of the existing offline approaches, we propose a novel online algorithm that makes cache placement and video scheduling decisions upon the arrival of each new request. Extensive simulations results demonstrate the significant performance improvement of the proposed strategy over traditional approaches in terms of cache hit ratio increase, backhaul traffic and initial access delay reduction.

Region of Feasibility of Interference Alignment in Underwater Sensor Networks

To enable underwater applications such as coastal and tactical surveillance, undersea explorations, and real-time picture/video acquisition, there is a need to achieve high data-rate and reliable communications underwater, which translates into attaining high acoustic channel spectral efficiencies. Interference alignment (IA), which has been recently proposed for radio-frequency multiple-input-multiple-output (MIMO) terrestrial communication systems, aims at improving the spectral efficiency by enabling nodes to transmit data simultaneously at a rate equal to half of the interference-free channel capacity. The core of IA in the space domain lies in designing transmit precoding matrices for each transmitter such that all the interfering signals align at the receiver along a direction different from that of the desired signal. While promising, however, there are still challenges to solve for the practical use of IA underwater, i.e., imperfect acoustic channel knowledge, high computational complexity, and high communication delay. In this paper, a feasibility study on the employment of IA underwater is presented. A novel distributed computing framework for sharing processing resources in the network so to parallelize and speed IA algorithms up is proposed; also, such framework enables “ensemble learning” of various precoding matrices computed using different (competing) IA algorithms so to achieve efficient alignment of the interference at the receiver. The robustness of the IA technique against imperfect acoustic channel knowledge is also quantified by estimating precoding matrices based on predicted channel coefficients. Finally, the performance of an algorithm to predict the underwater acoustic channel impulse response is presented using real data sets.

Dynamic Collaboration Between Networked Robots and Clouds in Resource-Constrained Environments

Underwater mobile sensor networks such as Autonomous Underwater Vehicles (AUVs) or robots are envisioned to enable applications for oceanographic data collection, environmental and pollution monitoring, offshore exploration, and distributed tactical surveillance. These applications require running compute- and data-intensive algorithms that go beyond the capabilities of the individual AUVs that are involved in a mission. To execute these task-parallel algorithms in resource- and time-constrained environments, dynamic and reliable collaboration between local networked robots (e.g., AUVs) and remote public Clouds is needed. To this end, the heterogeneous sensing, computing, communication, and storage capabilities of local and remote resources are exploited to form a “loosely coupled” mobile Cloud, and a novel resource provisioning engine that dynamically takes decisions on “what” and “where” the tasks should be executed in the mobile Cloud is introduced. Comparison of benefits of collaboration between local and Cloud resources with purely local and centralized approaches are presented through exhaustive computer simulations.

Maestro: Orchestrating Concurrent Application Workflows in Mobile Device Clouds

A hybrid mobile/fixed device cloud that harnessessensing, computing, communication, and storage capabilities of mobile and fixed devices in the field as well as those of computing and storage servers in remote data centers is envisioned. Mobile device clouds can be harnessed to enable innovative applications that rely on real-time, in-situ processing of sensor data collectedin the field. To support concurrent mobile applications on the device cloud, a robust distributed computing framework, called Maestro, is proposed. The key components of Maestro are(i) a task scheduling mechanism that employs controlled task replication in addition to task reallocation for robustness and (ii) Dedup for task deduplication among concurrent workflows. Experimental evaluation via prototype testbed of Android- and Linux-based mobile devices as well as simulations is performedto demonstrate Maestros capabilities.

MobiDiC: Exploiting the untapped potential of mobile distributed computing via approximation

Mobile computing is one of the largest untapped reservoirs in todays pervasive computing world as it has the potential to enable a variety of in-situ, real-time applications. Yet, this computing paradigm suffers when the available resources — such as device battery, CPU cycles, memory, I/O data rate — are limited. In this paper, the new paradigm of approximate computing is proposed to harness such potential and to enable real-time computation-intensive mobile applications in resource-limited and uncertain environments. A reduction in time and energy consumed by an application is obtained via approximate computing by decreasing the amount of computation needed by different tasks in an application; such improvement, however, comes with the potential loss in accuracy. Hence, a Mobile Distributed Computing framework, MobiDiC, is introduced to determine offline the ‘approximable’ tasks in an application and a light-weight algorithm is devised to select the approximate version of the tasks in an application during run-time. The effectiveness of the proposed approach is validated through extensive simulation and testbed experiments by comparing approximate versus exact-computation performance.

Distributed Computing Framework for Underwater Acoustic Sensor Networks

The goal of this paper is to enable near-realtime acquisition and processing of high resolution, high-quality, heterogeneous data from mobile and static sensing platforms to advance ocean exploration by providing infrastructure for a distributed computing framework. Reaching this goal will improve the efficiency of monitoring dynamic oceanographic phenomena such as phytoplankton growth and rate of photosynthesis, salinity and temperature gradient, and concentration of pollutants. resource provisioning framework for organizing the heterogeneous sensing, computing, and communication capabilities of static and mobile devices in the vicinity in order to form an elastic resource pool a hybrid static/mobile computing grid is presented. This local computing grid can be harnessed to enable innovative data- and compute-intensive mobile applications such as onshore near-real-time data processing, analysis and visualization, mission planning and online ocean adaptive sampling.

An Adaptive Sampling Solution using Autonomous Underwater Vehicles

Abstract To achieve efficient and cost-effective sensing coverage of the vast under-sampled 3D aquatic volume, intelligent adaptive sampling strategies involving Autonomous Underwater Vehicles (AUVs) endowed with underwater wireless (acoustic) communication capabilities are essential. These AUVs should coordinate and steer through the region of interest, and cooperatively sense, preprocess and transmit measured data to onshore stations for processing and analysis. Given a scalar field to sample, i.e, a phenomenon like temperature or salinity distribution, the AUVs should coordinate to take measurements using minimal resources (time or energy) in order to reconstruct the field with admissible error. A novel adaptive sampling solution to minimize the sampling cost is proposed, which requires the AUVs to take a small number of samples from the field. We observe via simulations that our solution outperforms existing solutions that are based on Compressive Sensing (CS) techniques.

A distributed adaptive sampling soluting using autonomous underwater vehicles

To achieve efficient and cost-effective sensing coverage of the vast under-sampled 3D aquatic volume, intelligent adaptive sampling strategies involving a team of Autonomous Underwater Vehicles (AUVs) endowed with underwater wireless communication capabilities become essential. Given a 3D field of interest to sample, the AUVs should coordinate to take measurements using minimal resources (time or energy) in order to reconstruct the field at an onshore station with admissible error. A novel distributed adaptive sampling solution that can minimize the sampling cost (in terms of time or energy expenditure) is proposed along with underwater acoustic communication protocols that facilitate the coordination of the vehicles. The proposed solution operates in two distinct phases in which it employs random compressive sensing (Phase I) and adaptive sampling (Phase II). Phase I captures the spatial distribution of the field of interest while Phase II tracks the temporal variation of the same. A distributed framework for multi-vehicle adaptive sampling that facilitates the movement of data between AUVs and enables compute intensive adaptive sampling algorithms is proposed. Simulation results on real data traces show that the proposed adaptive sampling solution significantly outperforms existing solutions in terms of reconstruction accuracy and energy expenditure.