Publications

2024

1. [ATC] TileClipper: Lightweight Selection of Regions of Interest from Videos for Traffic Surveillance Shubham Chaudhary, Aryan Taneja, Anjali Singh, Purbasha Roy, Sohum Sikdar, Mukulika Maity, and Arani Bhattacharya In USENIX Annual Technical Conference 2024 Abs PDF [Code]

   With traffic surveillance increasingly used, thousands of cameras on roads send video feeds to cloud servers to run computer vision algorithms, requiring high bandwidth. State-of-the-art techniques reduce the bandwidth requirement by either sending a limited number of frames/pixels/regions or relying on re-encoding the important parts of the video. This imposes significant overhead on both the camera side and server side compute as re-encoding is expensive. In this work, we propose TILECLIPPER, a system that utilizes tile sampling, where a limited number of rectangular areas within the frames, known as tiles, are sent to the server. TILECLIPPER selects the tiles adaptively by utilizing its correlation with the tile bitrates. We evaluate TILECLIPPER on different datasets having 55 videos in total to show that, on average, our technique reduces ≈ 22% of data sent to the cloud while providing a detection accuracy of 92% with minimal calibration and compute compared to prior works. We show real-time tile filtering of TILECLIPPER even on cheap edge devices like Raspberry Pi 4 and nVidia Jetson Nano. We further create a live deployment of TILECLIPPER to show that it provides over 87% detection accuracy and over 55% bandwidth savings.

2. [COMCOM] Hybrid CSMA/CA and HCCA uplink medium access control protocol for VLC based heterogeneous users Saswati Paramita, Arani Bhattacharya, Vivek Ashok Bohara, and Anand Srivastava In Computer Communications 2024 Abs PDF

   Light fidelity (LiFi) is an emerging wireless networking technology of visible light communication (VLC) paradigm for multiuser communication. This technology enables high data rates due to the availability of large visible light spectrum. While current studies have shown the potential for LiFi technology, they borrow the MAC-layer protocols from traditional WiFi. However, a number of prior studies have shown the challenges faced by the MAC-layer of WiFi in the presence of large number and types of devices. In this work, we show that the hybrid-coordination-function-controlled-access (HCCA) MAC protocol in LiFi provides higher throughput than the traditional CSMA/CA mechanism to user devices. We also show that HCCA has the limitation of higher message overhead in the presence of a large number of devices. We also evaluate the collision probability, busy channel probability, and delay for HCCA and CSMA/CA MAC protocol. We utilize both theoretical analysis and extensive simulations to study these performance tradeoffs and identify a threshold when a LiFi access point should switch to HCCA from CSMA/CA and vice-versa. Finally, based on our findings, we design a hybrid-MAC mechanism that switches between HCCA and CSMA/CA based on the number and type of devices present. Our evaluation shows that this hybrid mechanism can outperform both HCCA and CSMA/CA individually in the presence of different number of devices.

3. [TMA] Selection of Landmarks for Efficient Active Geolocation Shinyoung Cho, Zachary Weinberg, Arani Bhattacharya, Sophia Dai, and Ramsha Rauf In IFIP Network Traffic Measurement and Analysis Conference 2024 Abs PDF PPT

   A reliable way of estimating the location of an Internet host is to infer it from packet round-trip times between that host (the target) and several hosts in known locations (the landmarks). This technique is known as active geolocation. A major drawback of active geolocation is that it can be very slow, especially when many targets need to be located and when the landmarks are far away from the targets. In this work, we seek to improve the efficiency of active geolocation, by minimizing the number of landmarks used to locate a set of targets. We evaluate several algorithms for selecting an optimal set of landmarks from a larger pool: purely random selection, clustering based on geography and network topology, and incremental addition of landmarks far from those already used, according to two different distance metrics. We find that the most effective method is initial random selection of 100 landmarks, followed by incremental addition of landmarks while maximizing the Autonomous System (AS) and geographic diversity of the pool. Using this method, we can verify the location of a target using only 32% of a pool of 780 landmarks, with the same accuracy as if the entire pool had been used. After deanonymization, we will make our code publicly available for others to use and improve upon.

4. [VTC] WiLiConnect: A Novel CSI Sharing Technique in Hybrid WiFi/LiFi Networks Saswati Paramita, Arani Bhattacharya, Vivek Ashok Bohara, and Anand Srivastava In IEEE Vehicular Technology Conference 2024 Abs PDF

   In recent years, LiFi has become increasingly popular as an indoor communication technology that utilizes the unlicensed visible light and infra-red spectrum to transmit data. A major challenge of utilizing LiFi is that its area of coverage is limited. Thus, a large number of LiFi access points (APs) is often complemented by deploying a WiFi AP to form a hybrid WiFi/LiFi network. However, such deployment does not lead to any additional improvement in the performance of WiFi or LiFi APs. Recent WiFi APs are known to have high overhead due to the requirement of channel state information (CSI), which is essential for utilizing spatial multiplexing. Thus, in this work, we propose a system called WiLiConnect (WiFi-LiFi Connectivity with CSI), which communicates the CSI requirement of the WiFi channel through the LiFi APs, thereby reducing the overhead of WiFi APs. We formulate this problem of load-balancing the overhead of CSI sharing across the LiFi APs, and show that the general problem is NP-Hard. We then propose a round-robin algorithm to solve a special case of the problem, where all the users are assumed to have a single antenna. We further utilize extensive simulation to show that WiLiConnect significantly reduces the overhead of sending CSI. Specifically, WiLiConnect incurs only 0.06% overhead on a WiFi AP having 8 antennas on the total sum rate.

5. [EuroS&P] How Many Hands in the Cookie Jar? Examining Privacy Implications of Popular Apps in India Koustuv Kanungo, Aairah Bari, Rahul Khatoliya, Vishrut Arora, Arani Bhattacharya, Mukulika Maity, and Sambuddho In IEEE European Security and Privacy Symposium 2024 Abs PDF

   Smartphone app usage has steeply risen in India in the past decade. But limited efforts in the past assess the privacy aspects of these smartphone apps. Many of these are used for common utilities and handle sensitive user data. Such sensitive data leaks can have a wide variety of consequences when exposed to untrusted players (e.g., repressive governments, data/content hosting companies, and other third parties). These could range from mere embarrassment to personal targeting and surveillance. This paper presents a measurement study on the data collection and privacy considerations of some of the most popular apps on the Indian Google Play Store. We analyzed the apps’ data collection behavior on phones as well as the security of the servers to whom they send the data. We observed and categorized the data collected and transmitted to the backend hosting servers. This involved obfuscating the data transmitted and checking whether the app operations still function.Interestingly, for the non-government apps we found that extensive (mostly personally identifiable information), often “unnecessary”, data collection was being performed. In other words, we observed that a lot of these apps work fine even without these pieces of sensitive information. Furthermore, we found that often such sensitive data may be available in plaintext to the intermediate players managing/deploying the hosting infrastructure. We also found that while the governmental services-based apps collect fewer such unnecessary data users, they often store the data on web-fronted back-end databases with little to no user authentication mechanisms enabled. We expect our study to enable better understanding among both users and app developers about the privacy implications of these data collection practices.

6. [WiP@PERCOM] FlexDisplay: A Flexible Display Framework To Conserve Smartphone Battery Power Anshak Goel, Deeptorshi Mondal, Manavjeet Singh, Sahil Goyal, Navneet Agarwal, Jian Xu, Mukulika Maity, and Arani Bhattacharya In IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events 2024 Abs PDF PPT

   Despite significant improvements, smartphones are still constrained by the limited capacity of their batteries. Modern smartphones tend to use organic light-emitting diode (OLED) displays, whose energy consumption depends both on the brightness and the color content. Since the display of smartphones is known to consume a significant portion of this energy, a number of prior systems have tried to reduce screen brightness, increase areas of dark zones on the screen or use colors that consume less energy to mitigate this problem. However, the amount of energy savings using these techniques are still limited, as the underlying compute required to render the content still consumes energy. In this work, we provide a framework FlexDisplay that disables the display of a limited portion of the app content, saves the underlying compute needed to render the content as well as the touch sensors in the corresponding display area. FlexDisplay supports disabling of content across multiple apps. We demonstrate it on 15 apps over different genres and show that the energy savings vary from 10%–47% of the total energy consumption of the smartphone, depending on the app and the disabled content.

7. [COMSNETS] Roadside Traffic Monitoring using Video Processing on the Edge Saumya Jaipuria, Ansuman Banerjee, and Arani Bhattacharya In International Conference on Communication Systems & Networks 2024 Abs PDF

   Roadside traffic monitoring is increasingly performed by deploying roadside high-resolution video cameras and then running computer vision (CV) models on the video data. Since computer vision models are compute-intensive as they utilize deep neural networks (DNNs), the data is usually sent to one or more edge servers located adjacent to mobile base stations, thereby keeping the in-situ (on camera) processing load as less as possible. Recent techniques propose running CV models on tiles of videos separately to detect and track small objects. Several CV models exist, each with different requirements of compute and memory. Since more compute and memory-intensive CV models provide higher accuracy, a key challenge of such techniques is to determine which vision model should be used on which tile. This becomes even more challenging if multiple videos are processed by the same edge server. In this paper, we first formulate this problem of model selection and tile allocation as an Integer Linear Programming (ILP) instance, and then propose an approximation algorithm based on linear relaxation followed by randomized rounding to solve it. We present experimental results of our methods on an open source dataset based on trace-driven simulation to show that it gives result fast enough while also reducing execution time in a variety of scenarios.

2023

1. [COMSNETS] Multitask Scheduling of Computer Vision Workload on Edge Graphical Processing Units Arani Bhattacharya, Paritosh Shukla, Ansuman Banerjee, Saumya Jaipuria, Nanjangud C. Narendra, and Dhruv Garg In International Conference on Communication Systems & Networks 2023 Abs PDF PPT

   The increasing urbanization in developing countries calls for more efficient and safer transportation systems. A key technique used to enhance such efficiency and/or safety is to utilize running of computer vision algorithms to identify obstructions that may come up, and notify vehicles in real-time. Such real-time detection and notification requires sufficient computation resources located logically and physically close to the cameras. While utilization of edge compute devices has been proposed in the literature, it is unclear how such devices with heterogeneous processing units can handle real-time detection while multi-tasking. In this work, we profile the performance of a few devices with embedded and desktop-quality GPUs, and show that the performance while multi-tasking can be modeled as a submodular function. We utilize this observation to model load-balancing of camera videos as an instance of a submodular welfare problem, and solve it using a greedy algorithm. Our extensive trace-driven simulations show that our technique outperforms the baseline by over 40%.

2022

1. [COMSNETS] Fast Efficient Online Selection of Sensors for Transmitter Localization Arani Bhattacharya, Abhishek Maji, Jaya Prakash Verma Champati, and James Gross In International Conference on Communication Systems & Networks 2022 Abs PDF PPT

   The increase in cost and usage of RF spectrum has made it increasingly necessary to monitor its usage and protect it from unauthorized use. A number of prior studies have designed algorithms to localize unauthorized transmitters using crowdsourced sensors. To reduce the cost of crowdsourcing, these studies select the most relevant sensors a priori to localize such transmitters. In this work, we instead argue for online selection to localize such transmitters. Online selection can lead to more accurate localization using limited number of sensors, as compared to selecting sensors a priori, albeit at the cost of higher latency. To account for the trade-off between accuracy and latency, we a constraint on the number of selection rounds. For the case where the number of rounds is equal to the number of selected sensors, we propose a heuristic based on Thompson Sampling and show using trace-driven simulation that it provides 23% better accuracy compared to a number of proposed baseline algorithms. For restricted number of rounds, we show that using conventional parallel version of the modified Thompson Sampling which selects equal number of sensors in each round results in a substantial reduction in accuracy. To this end, we propose a strategy of selecting decreasing number of sensors in subsequent rounds of the modified Parallel Thompson Sampling. Our evaluation shows that the proposed heuristic leads to only 3% reduction in accuracy in contrast to 22% using modified Parallel Thompson Sampling, when we select 50 sensors in 20 rounds.

2021

1. [DYSPAN] A Gaussian Process Based Technique of Efficient Sensor Selection for Transmitter Localization Aritrik Ghosh, and Arani Bhattacharya In IEEE International Symposium on Dynamic Spectrum Access Networks 2021 Abs PDF PPT

   Spectrum monitoring via crowdsourcing is a technique that promises to enable opportunistic spectrum access. Crowdsourcing aims to provide incentives to users to deploy a large number of cheap but potentially noisy sensors. The sensors all send their data to a fusion center, where typically some algorithms are used to remove the noise from the data. Such crowdsourced monitoring of spectrum has been shown to be feasible in practice in multiple studies. One of the key goals of such monitoring is to identify any users that are violating the protocols of accessing spectrum. While a number of crowdsourcing techniques to identify such violations have been proposed, a key challenge that remains is to minimize the cost of data consumption and energy of running the sensors. In this work, we propose sequential probing of sensors to accurately localize/identify such transmitters. We formulate this as a Gaussian Process multi-armed bandit problem, and use a widely known solution technique called Upper Confidence Bound to solve it. We next observe that such sequential probing incurs additional latency, and use batched selection of sensors in few rounds to reduce latency. We show that instead of naively selecting sensors in parallel batches, an intelligent technique of selecting sensors called Gaussian Process Adaptive Upper Confidence Bound (GP-AUCB) can lead to selection of sensors that can lead to more accurate localization. Finally, we show the tradeoff between accuracy of localization, latency incurred and number of selected sensors via simulations.

2. [AICHALLENGEIoT@Sensys] Sensor Virtualization for Efficient Sharing of Mobile and Wearable Sensors Jian Xu, Arani Bhattacharya, Aruna Balasubramanian, and Donald E. Porter In International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things, In Conjunction with Sensys 2021 Abs PDF

   Users are surrounded by sensors that are available through various devices beyond their smartphones. However, these sensors are not fully utilized by current end-user applications. A key reason sensor use is so limited is that application developers must exactly identify how the sensor data can be used by smartphone apps. To mitigate this problem, we present SenseWear, a sensor-sharing platform that extends the functionality of a smartphone to use remote sensors with limited additional developer effort. Sensor sharing has several uses, including augmenting the hardware in smartphones, creating new gestural interactions with smartphone applications, and improving application’s Quality of Experience via higher-quality sensors from other devices, such as wearables. We developed and present six use cases that use remote sensors in various smartphone applications. Each extension requires adding fewer than 20 lines of code on average. Furthermore, using remote sensors did not introduce a perceptible increase in latency, and creates more convenient interaction options for smartphone apps.

3. [TON] Selection of Sensors for Efficient Transmitter Localization (Extended version of paper published in Infocom 2020) Arani Bhattacharya, Caitao Zhan, Abhishek Maji, Himanshu Gupta, Samir R. Das, and Petar M. Djuric In IEEE/ACM Transactions on Networking 2021 Abs PDF [Code]

   We address the problem of localizing an (unauthorized) transmitter using a distributed set of sensors. Our focus is on developing techniques that perform the transmitter localization in an efficient manner, wherein the efficiency is defined in terms of the number of sensors used to localize. Localization of unauthorized transmitters is an important problem which arises in many important applications, e.g., in patrolling of shared spectrum systems for any unauthorized users. Localization of transmitters is generally done based on observations from a deployed set of sensors with limited resources, thus it is imperative to design techniques that minimize the sensors’ energy resources. In this paper, we design greedy approximation algorithms for the optimization problem of selecting a given number of sensors in order to maximize an appropriately defined objective function of localization accuracy. The obvious greedy algorithm delivers a constant-factor approximation only for the special case of two hypotheses (potential locations). For the general case of multiple hypotheses, we design a greedy algorithm based on an appropriate auxiliary objective function—and show that it delivers a provably approximate solution for the general case. We develop techniques to significantly reduce the time complexity of the designed algorithms by incorporating certain observations and reasonable assumptions. We evaluate our techniques over multiple simulation platforms, including an indoor as well as an outdoor testbed, and demonstrate the effectiveness of our designed techniques—our techniques easily outperform prior and other approaches by up to 50-60% in large-scale simulations and up to 16% in small-scale testbeds.

4. [ICWS] User Allocation in Mobile Edge Computing: A Deep Reinforcement Learning Approach Subrat Prasad Panda, Ansuman Banerjee, and Arani Bhattacharya In IEEE International Conference on Web Services 2021 Abs PDF

   In recent times, the need for low latency has made it necessary to deploy application services physically and logicallyclose to the users rather than using the cloud for hosting services. This paradigm of computing, known as edge or fog computing,is becoming increasingly popular. An edge user allocation policy determines how to allocate service requests from mobile users to MEC servers. Current state-of-the-art techniques assume that the total resource utilization on an edge server is equal to the sum of the individual resource utilizations of services provisioned from the edge server. However, the relationship between resources utilized on an edge server with the number of service requests served from there is usually highly non-linear, hence, mathematically modelling the resource utilization is challenging. This is especially true in case of an environment with CPU-GPU co-execution, as commonly observed in modern edge computing. In this work, we provide an on-device Deep Reinforcement Learning (DRL) framework to predict the resource utilization of incoming servicerequests from users, thereby estimating the number of users an edge server can accommodate for a given latency threshold. We further propose an algorithm to obtain the user allocation policy. We compare the performance of the proposed DRL framework with traditional allocation approaches and show that the DRLframework outperforms deterministic approaches by at least 10% in terms of the number of users allocated.

5. [TNSM] Mosaic: Advancing User Quality of Experience in 360-Degree Video Streaming with Machine Learning (Extended version of paper published in IFIP Networking 2019) Sohee Park, Arani Bhattacharya, Zhibo Yang, Samir R Das, and Dimitris Samaras In IEEE Transactions on Network Service and Management 2021 Abs PDF

   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 \adapvr 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 standards-compliant components and provide a comprehensive performance evaluation of \adapvr along with five other streaming techniques – two for conventional adaptive video streaming and three for 360-degree tile-based video streaming. \adapvr outperforms the best of the competitions by as much as 47-191% in terms of average video quality of experience. Simulation based evaluation as well as subjective user studies further confirm the superiority of the proposed approach.

6. [WACV] Adaptive Streaming of 360-Degree Videos with Reinforcement Learning Sohee Park, Minh Hoai, Arani Bhattacharya, and Samir R. Das In Winter Conference on Applications of Computer Vision 2021 Abs PDF

   For bandwidth-efficient streaming of 360-degree videos, the streaming technique must adapt both to the changing viewport of the user and variations of the available network bandwidth. The state-of-the-art streaming techniques for this problem attempt to solve an optimization using simplied rules that do not adapt very well to the uncertainties related to the viewport or network. We adopt a 3D-Convolutional Neural Networks (3DCNN) model to extract spatio-temporal features of videos and predict the viewport. Given the sequential decision-making nature of such streaming technique, we then apply a Reinforcement Learning (RL) based adaptive streaming approach. We address the challenges of using RL in this scenario, such as large action space and delayed re-ward evaluation. Comprehensive evaluations with real net-work traces show that the proposed method outperforms three tile-based streaming techniques for 360-degree videos. Compared to the tile-based streaming techniques, the average user-perceived bitrate of the proposed method is 1.3–1.7 times higher and the average quality of experience of the proposed method is also 1.6–3.4 times higher. Subjective user studies further conrm the superiority of the proposed approach.

2020

1. [ICSOC] Mobility-Aware Service Placement for Vehicular Users in Edge-Cloud Environment Rahul Mudam, Saurabh Bhartia, Soumi Chattopadhyay, and Arani Bhattacharya In International Conference on Service Oriented Computing (ICSOC) 2020 Abs PDF

   In the era of Internet-of-things (IoT), both the number of web services and the number of users invoking them are increasing everyday. These web services utilize a cloud server for access to sufficient compute resources for service delivery. A disadvantage of cloud computing is that it is known to have a high latency because of its large distance (both physical distance as well as number of hops) from the end-user device. A key technique of enabling low-latency web services, called edge computing, brings the compute resources closer to the end device. Edge computing enables better resource utilization and it reduces latency. However, since there are numerous compute resources or ‘edge resources’, determining where the services should be placed becomes a new challenge. In this paper, we consider the case of public transport vehicles utilizing edge computing to reduce latency while providing such web services. We first model the dynamic service placement problem considering user mobility. We then propose two algorithms to solve this problem. The first algorithm utilizes an Integer Linear Programming (ILP) to obtain an optimal solution, albeit at the cost of scalability. We then propose a heuristic algorithm to achieve a low latency, while also scaling to large problem instances. We validate the performance of both the techniques through extensive trace-driven simulations.

2. [WOWMOM] LSTM-based GNSS Spoofing Detection Using Low-cost Spectrum Sensors Roberto Calvo-Palomino, Arani Bhattacharya, Gerome Bovet, and Domenico Giustiniano In IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WOWMOM) 2020 Abs PDF

   GNSS/GPS is a positioning system widely used nowadays in our lives for real-time localization in Earth. This technology is highly vulnerable to spoofing/jamming attacks caused by malicious intruders. In the recent years, commodity and low-cost radio-frequency hardware have been used to interfere with the legitimate GPS signal. Existing spoofing detection solutions use costly receivers and computationally expensive algorithms which limit the large-scale deployment. In this work we propose a GNSS spoofing detection system that can run on spectrum sensors with Software-Defined Radio (SDR) capabilities and cost in the order of 20 euros. Our approach exploits the predictability of the Doppler characteristics of the received GPS signals to determine the presence of anomalies or malicious attackers. We propose an artificial recurrent neural network (RNN) based on Long short-term memory (LSTM) for anomaly detection. We use data received by low-cost SDR receivers that are processed locally by low-cost embedded machines such as Nvidia Jetson Nano to provide inference capabilities. We show that our solution predicts very accurately the Doppler shift of GNSS signals and can determine the presence of a spoofing transmitter.

3. [WEB] An Intent-Based Automation Framework for Securing Dynamic Consumer IoT Infrastructures Vasudevan Nagendra, Arani Bhattacharya, Vinod Yegneswaran, Amir Rahmati, and Samir R. Das In The Web Conference 2020 Abs PDF

   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 14.2× 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.

4. [INFOCOM] Selection of Sensors for Efficient Transmitter Localization Arani Bhattacharya, Caitao Zhan, Himanshu Gupta, Samir R. Das, and Petar M. Djuric In IEEE International Conference on Communications (INFOCOM) 2020 Abs PDF PPT Preprint Video

   We address the problem of localizing an (illegal) transmitter using a distributed set of sensors. Our focus is on developing techniques that perform the transmitter localization in an efficient manner. Localization of illegal transmitters is an important problem which arises in many important applications. Localization of transmitters is generally done based on observations from a deployed set of sensors with limited resources, thus it is imperative to design techniques that minimize the sensors’ energy resources. In this paper, we design greedy approximation algorithms for the optimization problem of selecting a given number of sensors in order to maximize an appropriately defined objective function of localization accuracy. The obvious greedy algorithm delivers a constant-factor approximation only for the special case of two hypotheses (potential locations). For the general case of multiple hypotheses, we design a greedy algorithm based on an appropriate auxiliary objective function—and show that it delivers a provably approximate solution for the general case. We evaluate our techniques over multiple simulation platforms, including an indoor as well as an outdoor testbed, and demonstrate the effectiveness of our designed techniques—our techniques easily outperform prior and other approaches by up to 50-60% in large-scale simulations.

5. [INFOCOM] Streaming 360 Videos using Super-resolution Mallesham Dasari, Arani Bhattacharya, Santiago Vargas, Pranjal Sahu, Aruna Balasubramanian, and Samir R. Das In IEEE International Conference on Communications (INFOCOM) 2020 Abs PDF PPT

   360 videos provide an immersive experience to users, but require considerably more bandwidth to stream compared to regular videos. State-of-the-art 360◦ video streaming systems use viewport prediction to reduce bandwidth requirement, that involves predicting which part of the video the user will view and only fetching that content. However, viewport prediction is error prone resulting in poor user QoE. We design PARSEC, a 360 video streaming system that reduces bandwidth requirement while improving video quality. PARSEC trades off bandwidth for more client compute to achieve its goals. PARSEC uses a compression technique based on super resolution, where the video is significantly compressed at the server and the client runs a deep learning model to enhance the video to a much higher quality. PARSEC addresses a set of challenges associated with using super resolution for 360 video streaming: large deep learning models, high inference latency, and variance in the quality of the enhanced videos. To this end, PARSEC trains small micro-models over shorter video segments, and then combines traditional video encoding with super resolution techniques to overcome the challenges. We evaluate PARSEC on a real WiFi network, over a broadband network trace released by FCC, and over a 4G/LTE network trace.

2019

1. [DOCTORAL THESIS] Towards Performance Guarantees in Emerging Wireless Network Applications Arani Bhattacharya Abs PDF PPT

   The growing interest in mobile systems and Internet-of-Things (IoT) has engendered complex technical challenges ranging from efficient utilization of radio spectrum to developing applications on platforms widely varying in computational power and connectivity. Scalability is increasingly emphasized with exploding number of connected devices, complexity of applications and network data demands with proportionate pressure on limited radio spectrum resources. Our work picks two specific problem domains and explores algorithms that provide performance bounds while scaling to large problem instances. The domains we target are related to distributed radio spectrum monitoring and computation offloading.
   In distributed spectrum monitoring we target the spectrum patrolling problem where unauthorized transmitters are localized using a distributed set of spectrum sensors. We specifically consider a crowdsourcing model where a large number of inexpensive sensors are deployed to monitor the radio spectrum. We address different versions of transmitter detection and localization problems, specifically considering the limited budget for the sensors. We develop algorithms to reduce the cost of running a crowdsourced spectrum monitoring system and improve the accuracy of transmitter detection and localization. We also develop FPGA-based spectrum sensors and benchmark the performance improvements in terms of lower latency and energy consumptions than conventionally used sensors that use commodity embedded processor boards.
   Our second problem domain is related to computation offloading from weakly powered devices to more powerful cloud servers. Because of the uncertainty inherent in wireless connectivity, and the presence of a large number of devices, deciding which part of the computation to offload or where to offload is challenging. To address this, we propose algorithms that optimize the process of offloading. We focus on providing probabilistic guarantees on the performance of offloaded applications in the presence of channel errors. We further suggest a technique to minimize the completion time of the offloaded application using a novel scheduling technique called task duplication. We show the effectiveness of our algorithm via trace-driven simulation.

2. [TCCN] Spectrum Patrolling with Crowdsourced Spectrum Sensors Arani Bhattacharya, Ayon Chakraborty, Samir R. Das, Himanshu Gupta, and Petar M. Djuric In IEEE Transactions on Cognitive Communications and Networking 2019 Abs PDF

   We use a crowdsourcing approach for RF spectrum patrolling, where heterogeneous, low-cost spectrum sensors are deployed widely and are tasked with detecting unauthorized transmissions in a collaborative fashion while consuming only a limited amount of resources. We pose this as a collaborative signal detection problem where the individual sensor’s detection performance may vary widely based on their respective hardware or software configurations, but are hard to model using traditional approaches. Still an optimal subset of sensors and their configurations must be chosen to maximize the overall detection performance subject to given resource (cost) limitations. We present the challenges of this problem in crowdsourced settings and present a set of methods to address them. The proposed methods use data-driven approaches to model individual sensors and develops mechanisms for sensor selection and fusion while accounting for their correlated nature. We present performance results using examples of commodity-based spectrum sensors and show significant improvements relative to baseline approaches.

3. [IFIP Networking] Advancing User Quality of Experience in 360-degree Video Streaming Sohee Park, Arani Bhattacharya, Zhibo Yang, Mallesham Dasari, Samir R Das, and Dimitris Samaras In IFIP Networking Conference 2019 Abs PDF

   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 standards-compliant 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.

4. [PAM] Spectrum Protection from Micro-Transmissions using Distributed Spectrum Patrolling Mallesham Dasari, Muhammad Bershgal Atigue, Arani Bhattacharya, and Samir R Das In Passive and Active Measurement 2019 Abs PDF

   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 use of small inexpensive 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 are very resource limited in terms of hardware, and thus the sensing parameters used in general, cannot be directly applied as is. To demonstrate this, we consider detecting an unauthorized transmitter that transmits intermittently for very small durations (micro-transmissions) as an example application. We quantify the impact of device hardware and crucial sensing parameters such as sampling rate, integration size and frequency resolution in detecting such transmissions. We find that we cannot detect more than 45% of micro-transmissions on these inexpensive spectrum sensors. To counter this, we show that almost all the micro-transmissions can be detected by deploying more sensors in a given location and fusing the data from multiple sensors.

5. [SOSR] Scalable and Resource Efficient Control Plane for Next Generation Cellular Packet Core Vasudevan Nagendra, Arani Bhattacharya, Anshul Gandhi, and Samir R Das In ACM Symposium on SDN Research 2019 Abs PDF

   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 MMELite, 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 prototype MMELite using DPDK and OpenNetVM frameworks and demonstrate its performance benefits over existing approaches with respect to SLO compliance and resource efficiency.

2018

1. [DYSPAN] Quantifying Energy and Latency Improvements of FPGA-Based Sensors for Low-Cost Spectrum Monitoring Arani Bhattacharya, Han Chen, Peter Milder, and Samir R Das In IEEE International Symposium on Dynamic Spectrum Access 2018 Abs PDF PPT

   There is a recent interest in large-scale RF spectrum monitoring using low-cost crowdsourced spectrum sensors. A major challenge here is improving the latency and energy usage of signal processing algorithms on the sensor. This improves operational cost and effectiveness and also makes the sensors more responsive to the monitoring task. We specifically consider the case of signal detection using such sensors. Typical crowdsourced implementation using a low-cost software radio connected to a Raspberry Pi or a smartphone as host is not energy-efficient and incurs significant latencies. We propose use of field-programmable gate array (FPGA) to improve both metrics for the signal detection task. Our benchmarking shows significant improvements with FPGA platforms relative to using a Raspberry Pi or smartphone, up to a factor of 73 in terms of latency and a factor of 29 in terms of energy usage.

2. [IMC] Impact of Device Parameters on QoE of Internet-based Mobile Applications Mallesham Dasari, Santiago Vargas, Arani Bhattacharya, Samir R Das, Aruna Balasubramanian, and Michael Ferdman In ACM Internet Measurement Conference 2018 Abs PDF

   A large fraction of users in developing regions use relatively inexpensive low-end smartphones. However, the impact of device capabilities on the performance of mobile Internet applications has not been explored. In order to bridge this gap we study the QoE of three popular applications: Web browsing, video streaming, and telephony, for different device parameters. Our results demonstrate that the Web applications are much more sensitive to low-end hardware compared to video applications, especially video streaming. This is because the video applications exploit specialized coprocessors/accelerators and thread level parallelism on multi-core mobile devices. Even low-end devices are equipped with coprocessors and multiple cores. Whereas, Web browsing is largely influenced by clock frequency and uses no more than 2 cores. This makes Web browsing more vulnerable to performance on low-end smartphones. Based on the lessons learnt from studying video applications, we explore offloading Web computation to a co-processor. Specifically, we explore offloading of regular expression computation to a DSP coprocessor and show an improvement of 18% in page load time while saving the energy by a factor of 4.

3. [INFOCOM] Spectrum Patrolling with Crowdsourced Spectrum Sensors Ayon Chakraborty, Arani Bhattacharya, Snigdha Kamal, Samir R Das, Himanshu Gupta, and Petar M Djuric In IEEE International Conference on Communications (INFOCOM) 2018 Abs PDF PPT

   We use a crowdsourcing approach for RF spectrum patrolling, where heterogeneous, low-cost spectrum sensors are deployed widely and are tasked with detecting unauthorized transmissions in a collaborative fashion while consuming only a limited amount of resources. We pose this as a collaborative signal detection problem where the individual sensor’s detection performance may vary widely based on their respective hardware or software configurations, but are hard to model using traditional approaches. Still an optimal subset of sensors and their configurations must be chosen to maximize the overall detection performance subject to given resource (cost) limitations. We present the challenges of this problem in crowdsourced settings and present a set of methods to address them. The proposed methods use data-driven approaches to model individual sensors and develops mechanisms for sensor selection and fusion while accounting for their correlated nature. We present performance results using examples of commodity-based spectrum sensors and show significant improvements relative to baseline approaches.

2017

1. A First Look at Performance of TV Streaming Sticks Ayon Chakraborty, Arani Bhattacharya, Santosh Ghosh, and Samir R. Das In IEEE 38th Sarnoff Symposium 2017 Abs PDF PPT

   Recent measurements show that more than half of the peak time Internet traffic is due to video streaming. Recent trends also suggest that consumers are increasingly receiving their TV content over the Internet via streaming appliances that are connected to the TV. We present the first systematic measurement study of a popular class of such devices that have the ‘stick’ form factor. The study covers streaming and network related performance using a widely used content server on the Internet (Netflix) and a local instrumented media server. On the user-end, we use three widely available mediasticks in the US - Chromecast , Amazon Fire and Roku . We observe that there are significant performance differentials across the streaming sticks. Our experiments show that Amazon Fire and Chromecast provide better user experience in the presence of varying network conditions, whereas Roku performs best at high stable bandwidth.

2. [JNCA] A Survey of Adaptation Techniques in Computation Offloading Arani Bhattacharya, and Pradipta De Journal of Network and Computer Applications 2017 Abs PDF

   Computation offloading is a method of saving energy and time on resource-constrained mobile devices by executing some tasks on the cloud. A computation offloading mechanism determines portions of the application that can be offloaded for remote execution. The offloading decision problem depends on various parameters, like application characteristics, network conditions, hardware features, that influence the operating environment of an offloading system. Variations in these parameter values make it challenging to design effective offloading solutions that can adapt gracefully to all the changes. Hence a number of work has focused on offloading solutions that can adapt to changes in the parameter values. In this paper, we survey adaptation techniques used by offloading systems. We provide a panoptic view of the task offloading problem by identifying the variable parameters in the offloading ecosystem, present offloading solutions that adapt to these parameters, and point out the associated improvements in Quality of Experience of users.

3. [CCNC] Scheduling with Task Duplication for Application Offloading Arani Bhattacharya, A. Banerjee, and Pradipta De In IEEE CCNC 2017 Abs PDF

   Computation offloading frameworks partition an application’s execution between a cloud server and the mobile device to minimize its completion time on the mobile device. An important component of an offloading framework is the partitioning algorithm that decides which tasks to execute on mobile device or cloud server. The partitioning algorithm schedules tasks of a mobile application for execution either on mobile device or cloud server to minimize the application finish time. Most offloading frameworks partition parallel applications devices using an optimization solver which takes a lot of time. We show that by allowing duplicate execution of selected tasks on both the mobile device and the remote cloud server, a polynomial algorithm exists to determine a schedule that minimizes the completion time. We use simulation on both random data and traces to show the savings in both finish time and scheduling time over existing approaches. Our trace-driven simulation on benchmark applications shows that our algorithm reduces the scheduling time by 8 times compared to a standard optimization solver while guaranteeing minimum makespan.

2016

1. [GLOBECOM] Service Level Guarantee for Mobile Application Offloading in Presence of Wireless Channel Errors Arani Bhattacharya, Ansuman Banerjee, and Pradipta De In IEEE Global Communications Conference (GLOBECOM) 2016 Abs PDF PPT

   Mobile cloud computing is increasingly being used in recent times to offload parts of an application to the cloud to reduce its finish time. However, quality of offloading decisions depend on network conditions and hence many offloading solutions assume that MAC layer retransmissions will tackle transient frame errors. This can lead to suboptimal solutions, as well as, degrade service level guarantee of reducing finish time compared to execution without offloading. In this work, we propose an error-aware solution that uses run-time channel conditions to adapt the offloading decisions. We guarantee that given a failure rate bound (ϵ), offloading decisions will achieve application execution in less time than that of local execution with a probability of (1-ϵ) while operating in networks with unpredictable error characteristics. Simulation results show that at channel error rate of 20%, our heuristic provides 90% guarantee of better performance than on-device computation and reduces the mean finish time by 18% compared to execution without any offloading.

2. [Workshop@PODC] Computation Offloading from Mobile Devices: Can Edge Devices Perform Better Than the Cloud? Arani Bhattacharya, and Pradipta De In Proceedings of the Third International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing 2016 Abs PDF

   Mobile devices like smartphones can augment their low-power processors by offloading portions of mobile applications to cloud servers. However, offloading to cloud data centers has a high network latency. To mitigate the problem of network latency, recently offloading to computing resources lying within the user’s premises, such as network routers, tablets or laptop has been proposed. In this paper, we determine the devices whose processors have sufficient power to act as servers for computation offloading. We perform trace-driven simulation of SPECjvm2008 benchmarks to study the performance using different hardware. Our simulation shows that offloading to current state-of-the-art processors of user devices can improve performance of mobile applications. We find that offloading to user’s own laptop reduces finish time of benchmark applications by 10%, compared to offloading to a commercial cloud server.

2015

1. [Workshop@PODC] Parametric Analysis of Mobile Cloud Computing Frameworks Using Simulation Modeling Arani Bhattacharya, Ansuman Banerjee, and Pradipta De In Adaptive Resource Management and Scheduling for Cloud Computing 2015 Abs PDF

   Mobile Cloud Computing (MCC) frameworks implement mechanisms for selecting tasks in an application and offloading those tasks for execution on a cloud server. Task partitioning and task offloading aim to optimize performance objectives, like lower energy usage on mobile devices, faster application execution, while operating even in unpredictable environments. Offloading decisions are influenced by several parameters, like varying degrees of application parallelism, variable network conditions, trade-off between energy saved and time to completion of an application, and even user-defined objectives. In order to investigate the impact of these variable parameters on offloading decision, we present a detailed model of the offloading problem incorporating these parameters. Implementations of offloading mechanisms in MCC frameworks often rely on only a few of the parameters to reduce system complexity. Using simulation, we analyze influence of the variable parameters on the offloading decision problem, and highlight the complex interactions among the parameters.

2014

1. Energy-Aware H.264 Decoding Arani Bhattacharya, Ansuman Banerjee, and Susmita Sur-Kolay In Distributed Computing and Internet Technology 2014 Abs PDF

   The increasing use of more resource-intensive multimedia applications in communication has made it essential to ensure better utilization of available computing resources. At the same time, energy consumption has turned out to be one of the most important resource constraints in modern systems. Digital videos are an important part of multimedia, and a large number of video standards are currently available. In this paper, we work on the most commonly used video standard named H.264. We propose a method to reduce the energy consumption involved in video decoding by selective degradation of video quality. Experiments on the LIVE video database show that our proposed method is quite effective in practice.

2013

1. A Cache-Aware Strategy for H.264 Decoding on Multi-processor Architectures Arani Bhattacharya, Ansuman Banerjee, Susmita Sur-Kolay, Prasenjit Basu, and Bhaskar J. Karmakar In VLSI Design and Test 2013 Abs PDF

   H.264-AVC is one of the most popular formats for the recording, compression and distribution of video. Encoders and decoders for the H.264 standard are widely in demand, and efficient strategies for enhancing their performance have been areas of active research. With the proliferation of many-core architectures in the embedded community, there has been a trend towards parallelizing implementations of encoders and decoders. In this paper, we present a run time heuristic which exploits macro-block level parallelism and efficient scheduling inside a H.264 decoder to reduce the number of cache misses and improve the processor utilization. Experiments on standard benchmarks show a significant speed-up over contemporary strategies proposed in literature.