ABOUT

PERSONAL DETAILS
rv355@cam.ac.uk
FN07, Department of Computer Science and Technology,
University of Cambridge, Cambridge, UK
Hello. I am a Programmer Researcher Programmer Researcher Programmer
I am passionate about programming and developing systems.
Welcome to my Personal profile

I am a Research Associate in the Systems Research Group at the Department of Computer Science and Technology, University of Cambridge
Prior to joining University of Cambridge, I was a PhD student in the Complex Network Research Group at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur under the supervision of Dr. Sandip Chakraborty and Dr. Bivas Mitra (2016-2020). I am a recipient of the TCS Research Fellowship (2016).

My primary area of research has been in the field of sensor data collection and analysis obtained from multi-modal sources. In my PhD I had been utilizing such information towards developing systems supporting the transport systems. Currently, I am more focused on the real-time aspect of the data being generated by sensors deployed at a citywide scale. The idea being not only to store and learn from the data that these citywide sensors generate but to take crucial decisions with minimum latency to support the needs of the city.


RESUME

  • CURRENT
  • Cambridge, UK

    RESEARCH ASSOCIATE

    Department of Computer Science and Technology, University of Cambridge

    WebPage: https://www.cst.cam.ac.uk/
  • EDUCATION
  • Kharagpur, India

    Doctor of Philosophy (PhD) - Computer Science and Engineering

    INDIAN INSTITUTE OF TECHNOLOGY (2016 - 2020)

    Thesis Title: Spatio-Temporal Data Collection and Analysis for Developing Transport related Services
    Supervisor: Dr. Sandip Chakraborty, Dr. Bivas Mitra
  • Durgapur, India

    Bachelor of Technology (B.Tech) - Computer Science and Engineering

    NATIONAL INSTITUTE OF TECHNOLOGY (2009 - 2013)

  • PROFESSIONAL EXPERIENCE
  • Bangalore, India

    PUBLICITY CO-CHAIR

    COMSNETS 2021

    WebPage: COMSNETS 2021
  • Kharagpur, India

    WEBMASTER

    IMOBILE: The India Chapter of ACM SIGMOBILE

    WebPage: IMOBILE
  • Kharagpur, India

    WEB CHAIR

    MobiCom 2018

    WebPage: MobiCom 2018
  • Khragpur, India

    WEB CHAIR

    COMSNETS 2018

    WebPage: COMSNETS
  • Khragpur, India

    JUNIOR RESEARCH FELLOW

    INDIAN INSTITUTE OF TECHNOLOGY (2015)

    Project: DiSARM
  • Bangalore, India

    SOFTWARE DEVELOPER

    SCHNEIDER ELECTRIC INDIA PVT. LTD. (2013 - 2015)

    .NET and ANDROID Development
  • Kharagpur, India

    INTERN

    INDIAN INSTITUTE OF TECHNOLOGY (2012)

    Guide: Dr. Niloy Ganguly
    Project Title: Analyzing a Fastest Path Algorithm for Spatio-Temporal Networks
  • Kharagpur, India

    INTERN

    INDIAN INSTITUTE OF TECHNOLOGY (2011)

    Guide: Dr. Niloy Ganguly
    Project Title: Delay Tolerant Network and its Application in Post Disaster Management
  • TEACHING ASSISTANTSHIP
  • Computing Lab, Dept of CSE, IIT Kharagpur (Spring 2018 and 2019)

  • Smartphone Computing, Dept of CSE, IIT Kharagpur (Autumn 2017)

  • Computer Networks Lab, Dept of CSE, IIT Kharagpur (Autumn 2016)

  • Programming and Data Structure, Dept of CSE, IIT Kharagpur (Spring 2016, Spring 2017, Autumn 2018)

  • HONORS AND AWARDS
  • TCS PhD Fellowship - 2016

  • Google Student Travel Grant for IEEE PerCom 2019

  • LRN Travel Grant for IEEE PerCom 2019

  • Google Student Travel Grant for ACM SIGSPATIAL 2018

  • XRCI Travel Grant for IEEE INFOCOM 2016

  • ACM IARCS Travel Grant for IEEE INFOCOM 2016

PUBLICATIONS

PUBLICATIONS LIST

Smartphones for Public Transport Planning and Recommendation in Developing Countries - A Review

WIREs Data Mining and Knowledge Discovery

Journal Paper R. Verma, S. Chakraborty

Smartphones for Public Transport Planning and Recommendation in Developing Countries - A Review

R. Verma, S. Chakraborty Journal Paper

In this era of connected systems that have penetrated everywhere, transport units have become a significant source of data, collected from commuters, vehicles, drivers, or any section being touched by the transport system. This data, which has both spatial as well as temporal aspects, is utilized for a plethora of services like travel assistant systems, multi-modal transport solutions, real-time travel information, smart parking, autonomous vehicles, to name a few. With the current buzz of sustainable transport, the use of public transport systems have been popularized owing to the economic and environmental savings. In this review paper, we provide a highlight of works which have tried to utilize techniques to improve multiple sections of the public transport system, primarily focusing on developing economies, thus improving the overall commute experience at various countries.

A Smartphone-based Passenger Assistant for Public Bus Commute in Developing Countries

IEEE Transactions on Computational Social Systems (2020)

Journal Paper PDF R. Verma, A. Shrivastava, K. De, B. Mitra, S. Saha, N. Ganguly, S. Nandi and S. Chakraborty

A Smartphone-based Passenger Assistant for Public Bus Commute in Developing Countries

R. Verma, A. Shrivastava, K. De, B. Mitra, S. Saha, N. Ganguly, S. Nandi and S. Chakraborty Journal Paper

Although public transports vehicles like buses have always been a cheap means of commuting in the cities of many developing countries, it is always considered as a secondary mode of transport owing to poor infrastructure, chaotic and reckless driving habits and absence of any proper information system in buses. Based on rigorous experiments carried over a period of two years and multiple surveys, we have tried to learn the problems faced by the bus commuters. As a solution, in this work, we develop a novel energy efficient system which would help commuters navigate through their journey safely. Along with making them aware of any upcoming points of concerns (PoC) like sudden bumps, sharp turns, bad roads etc, we also inform commuters about the expected time of arrival at the destination. The system makes use of several landmarks like speed breakers, turns and bus stops on a trail stored in a specialized data structure, the probabilistic timed automata. We conducted extensive experiments using 25 volunteers over 50 trails. The system showed an average localization error of only 50m and mean ETA error of 2.5 minutes and a fairly high alert prediction accuracy, while consuming significantly less energy when compared to GPS.

https://ieeexplore.ieee.org/abstract/document/8960419

Avoiding Stress Driving: Online Trip Recommendation from Driving Behavior Prediction

KYOTO - JAPAN

Proceedings of the 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom 2019)

Conferences PDF PPT R. Verma, B. Mitra and S. Chakraborty

Avoiding Stress Driving: Online Trip Recommendation from Driving Behavior Prediction

R. Verma, B. Mitra and S. Chakraborty Conferences

The growth in the market for cab companies like Uber has opened the door to high-income options for drivers. However, in order to boost their income, drivers many a time resort to accepting trips which increases their stress resulting in poor driving quality and accidents in serious cases. Every driver handles stress differently and the trip recommendation thus needs to be on a personalized level. In this paper, we explore historical trip data to compute the driving stress and its impact on various driving behavioral features, captured through vehicle-mounted GPS and inertial sensors. We utilize a Multi-task Learning based Neural Network model to learn both the common features and the personalized features from the driving data to predict the stress level of a driver. We further establish a causal relationship between the stress level of a driver and his driving behavior. Finally, we develop a trip recommendation system for cab drivers to avoid stress driving. The models have been tested over both a publicly available dataset with 6 drivers for 500 minutes of driving data and an in-house collected dataset from 8 drivers over 1700 trips for 5 months. We observe that the proposed model gives an average prediction accuracy of 94% with low false-positive rates. We also observed that the driving behavior is improved when a driver takes a recommended trip.

Mining Spatio-temporal Data for Computing Driver Stress and
Observing Its Effects on Driving Behavior

SEATTLE - USA

Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2018)

Conferences PDF R. Verma, G. Prajjwal, B. Mitra and S. Chakraborty

Mining Spatio-temporal Data for Computing Driver Stress and Observing Its Effects on Driving Behavior

R. Verma, G. Prajjwal, B. Mitra and S. Chakraborty Conferences

With the increase in road fatalities due to various factors like aggressive driving and road rage, quantifying and monitoring the stress level of a driver is an important task for the preparation of driving rosters for the cab companies. Stress monitoring using physiological sensors is a costly and obstructive task, while stress factors impact di‚erently for di‚erent individuals based on their personality traits. In this paper, we develop a learning-based model to predict the stress level of a driver and its e‚ect on his driving behavior, solely based on spatio-temporal driving data collected through GPS and inertial sensors. We further establish a correlation between the stress level of a driver and his driving behavior; thus, we develop a complete system to infer stress pro€ling and its impact on driving behavior based on spatio-temporal driving data. ‘e model has been tested over a publicly available dataset with 6 drivers for 500 minutes of driving data. We observe that the proposed model gives an average prediction accuracy of 79% with low false-positive rates.

ComfRide:A Smartphone based System for Comfortable Public Transport Recommendation

VANCOUVER - CANADA

Proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018)
Project page Link

Conferences PDF PPT R. Verma, S. Ghosh, M. Saketh, N. Ganguly, B. Mitra and S. Chakraborty

ComfRide:A Smartphone based System for Comfortable Public Transport Recommendation

R. Verma, S. Ghosh, M. Saketh, N. Ganguly, B. Mitra and S. Chakraborty Conferences

Passenger comfort is a major factor influencing a commuter’s decision to avail public transport. Existing studies suggest that factors like overcrowding, jerkiness, traffic congestion etc. correlate well to passenger’s (dis)comfort. An online survey conducted with more than 300 participants from 12 different countries reveals that different personalized and context dependent factors influence passenger comfort during a travel by public transport. Leveraging on these findings, we identify correlations between comfort level and these dynamic parameters, and implement a smartphone based application, ComfRide, which recommends the most comfortable route based on user’s preference honoring her travel time constraint. We use a ‘Dynamic Input/Output Automata’ based composition model to capture both the wide varieties of comfort choices from the commuters and the impact of environment on the comfort parameters. Evaluation of ComfRide, involving 50 participants over 28 routes in a state capital of India, reveals that recommended routes have on average 30% better comfort level than Google map recommended routes, when a commuter gives priority to specific comfort parameters of her choice.

Smart-phone based Spatio-temporal Sensing for Annotated Transit Map Generation

REDONDO BEACH - USA

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems(SIGSPATIAL 2017)

Conferences PDF PPT R. Verma, Surjya Ghosh, N. Ganguly, B. Mitra and S. Chakraborty

Smart-phone based Spatio-temporal Sensing for Annotated Transit Map Generation

R. Verma, Surjya Ghosh, N. Ganguly, B. Mitra and S. Chakraborty Conferences

City transit maps are one of the important resources for public navigation in today’s digital world. However, the availability of transit maps for many developing countries is very limited, primarily due to the various socio-economic factors that drive the private operated and partially regulated transport services. Public transports at these cities are marred with many factors such as uncoordinated waiting time at bus stoppages, crowding in the bus, sporadic road conditions etc., which also need to be annotated so that commuters can take informed decision. Interestingly, many of these factors are spatio-temporal in nature. In this paper, we develop CityMap, a system to automatically extract transit routes along with their eccentricities from spatio-temporal crowdsensed data collected via commuters’ smart-phones. We apply a learning based methodology coupled with a feature selection mechanism to filter out the necessary information from raw smart-phone sensor data with minimal user engagement and drain of battery power. A thorough evaluation of CityMap, conducted for more than two years over 11 different routes in 3 different cities in India, show that the system effectively annotates bus routes along with other route and road features with more than 90% of accuracy.

Unsupervised Annotated City Traffic Map Generation

SAN FRANCISCO - USA

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems(SIGSPATIAL 2016)
Project page Link

Conferences PDF POSTER R. Verma, Surjya Ghosh, A. Shrivastava, N. Ganguly, B. Mitra and S. Chakraborty

Unsupervised Annotated City Traffic Map Generation

R. Verma, Surjya Ghosh, A. Shrivastava, N. Ganguly, B. Mitra and S. Chakraborty Conferences

Public bus services in many cities in countries like India are controlled by private owners, hence, building up a database for all the bus routes is non-trivial. In this paper, we leverage smart-phone based sensing to crowdsource and populate the information repository for bus routes in a city. We have developed an intelligent data logging module for smart-phones and a server side processing mechanism to extract roads and bus routes information. From a 3 month long study involving more than 30 volunteers in 3 different cities in India, we found that the developed system, CrowdMap, can annotate bus routes with a mean error of 10m, while consuming 80% less energy compared to a continuous GPS based system.

Design Of Efficient Lightweight Strategies To Combat Dos Attack
In Delay Tolerant Network Routing

Wireless Networks, pp. 1-22
Springer US (2016)

Journal Paper PDF S. Saha, S. Nandi, R. Verma, S. Sengupta, K. Singh, V. Sinha, S.K. Das

Design Of Efficient Lightweight Strategies To Combat Dos Attack In Delay Tolerant Network Routing

S. Saha, S. Nandi, R. Verma, S. Sengupta, K. Singh, V. Sinha, S.K. Das Journal Paper

Delay tolerant networks (DTNs) are characterized by delay and intermittent connectivity. Satisfactory network functioning in a DTN relies heavily on co-ordination among participating nodes. However, in practice, such co-ordination cannot be taken for granted due to possible misbehaviour by relay nodes. Routing in a DTN is, therefore, vulnerable to various attacks, which adversely affect network performance. Several strategies have been proposed in the literature to alleviate such vulnerabilities—they vary widely in terms of throughput, detection time, overhead etc. One key challenge is to arrive at a tradeoff between detection time and overhead. We observe that the existing table-based reactive strategies to combat Denial-of-service (DoS) attacks in DTN suffer from two major drawbacks: high overhead and slow detection. In this paper, we propose three secure, light-weight and time-efficient routing algorithms for detecting DoS attacks (Blackhole and Grey-hole attacks) in the Spray & Focus routing protocol. The proposed algorithms are based on use of a small fraction of privileged (trusted) nodes. The first strategy, called TN, outperforms the existing table-based strategy with 20–30 % lesser detection time, 20–25 % higher malicious node detection and negligible overhead. The other two strategies, CTN_MI and CTN_RF explore the novel idea that trusted nodes are able to utilize each others’ information/experience using their long range connectivity as and when available. Simulations performed using an enhanced ONE simulator reveals that investing in enabling connectivity among trusted nodes (as in CTN_RF) can have significant performance benefits.

Margdarshak: A Mobile Data Analytics based Commute Time Estimator cum Route Recommender

SINGAPORE

Proceedings of the 3rd International on Workshop on Physical Analytics (WPA 2016)

Conferences PDF PPT R. Verma, A. Shrivastava, S. Chakraborty, B. Mitra

Margdarshak: A Mobile Data Analytics based Commute Time
Estimator cum Route Recommender

R. Verma, A. Shrivastava, S. Chakraborty, B. Mitra Conferences

Waiting at traffic signals and getting stuck in traffic congestion eats a lot of time for a commuter in most of the metro cities of the world. Although there exists a large pool of navigation applications, but all of them turn out to be ineffective for dynamically finding out the best route under uncertainty. In this work, we present Margdarshak, a navigation system which utilizes the impact of congestion and wait time at traffic signals for estimating the travel time over a route. We collected a month-long traffic data from different routes at five various cities in India for analyzing the problem in detail. The evaluations performed over the system show that Margdarshak gives a mean estimation error of ±1.5$ minutes, and performs significantly better under uncertainty, compared to other state of the art navigation systems like Google Maps, Here Maps and Waze.

UrbanEye: An outdoor localization system for public transport

SAN FRANCISCO - USA

Proceedings of The 35th Annual IEEE International Conference on Computer Communications (IEEE INFOCOM 2016)
Project page Link

Conferences PDF PPT R. Verma, A. Shrivastava, B. Mitra, S. Saha, N. Ganguly, S. Nandi, S. Chakraborty

UrbanEye: An outdoor localization system for public transport

R. Verma, A. Shrivastava, B. Mitra, S. Saha, N. Ganguly, S. Nandi, S. Chakraborty Conferences

Public transport in suburban cities (covers 80% of the urban landscape) of developing regions suffer from the lack of information in Google Transit, unpredictable travel times, chaotic schedules, absence of information board inside the vehicle. Consequently, passengers suffer from lack of information about the exact location where the bus is at present as well as the estimated time to be taken to reach the desired destination. We find that off-the-shelf deployment of existing (non-GPS) localization schemes exhibit high error due to sparsity of stable and structured outdoor landmarks (anchor points). Through rigorous experiments conducted over a month however, we realize that there are a certain class of volatile landmarks which may be useful in developing efficient localization scheme. Consequently, in this paper, we design a novel generalized energy-efficient outdoor localization scheme - UrbanEye, which efficiently combines the volatile and non-volatile landmarks using a specialized data structure, the probabilistic timed automata. UrbanEye uses speed-breakers, turns and stops as landmarks, estimates the travel time with a mean accuracy of ±2.5 mins and produces a mean localization accuracy of 50 m. Results from several runs taken in two cities, Durgapur and Kharagpur, reveal that UrbanEye provides more than 50% better localization accuracy compared to the existing system Dejavu, and consumes significantly less energy.

e-ONE: enhanced ONE for simulating challenged network scenarios

Journal of Networks 9.12, pp.3290-3304
Academy Publishers (2014)

Journal Paper PDF S. Saha, R. Verma, S. Saika, P.S. Paul, S. Nandi
S. Saha, R. Verma, S. Saika, P.S. Paul, S. Nandi Journal Paper

Delay Tolerant Network (DTN) empowers sparse mobile ad-hoc networks and other challenged network environments, such as interplanetary communication network or deep sea communication network, where traditional networking protocols either fail to work completely or do not work well. The Opportunistic Networking Environment (ONE) Simulator has gained considerable popularity as an efficient tool for validating and analysing DTN routing and application protocols. It provides options for creating different mobility models and routing strategies as per the users' requirements. Nowadays, challenged networks such as rural internet connection, social networks, post-disaster communication systems, etc. use DTN along with some hybrid infrastructure networks. Incorporating such real life network systems in ONE needs extensive modification of the same. In this paper, we present the enhanced ONE (e-ONE) simulator as an extension of ONE to facilitate simulation of challenged networks and describe the enhancements we have added to the ONE. As a case study, we consider a challenged network, which we call a latency aware 4-tier planned hybrid architecture designed for post-disaster management. We describe, in detail, how this enhanced version of the ONE simulator is useful in analysis and evaluation of the scenario considered.

Is It Worth Taking A Planned Approach To Design Ad
Hoc Infrastructure For Post Disaster Communication?

ISTANBUL - TURKEY

Proceedings of The Seventh ACM International Workshop on Challenged Networks (CHANTS 2012)

Conferences PDF S. Saha, V K Shah, R Verma, R. Mandal, S. Nandi

Is It Worth Taking A Planned Approach To Design Ad Hoc Infrastructure For Post Disaster Communication?

Sujoy Saha, Vijay Kumar Shah, Rohit Verma, Ratna Mandal, Subrata Nandi Conferences

After any natural disaster the availability of existing conventional communication infrastructure almost gets ruled out. After the devastation, to restore the communication system in ad hoc basis; ensuring almost 100% packet delivery within acceptable latency with optimal utilization of resources are prime design motives. Our work proposes a four tier planned hybrid architecture, which conforms the aforesaid motives yielding a desired performance in terms of delivery probability within least latency, for a given disaster hit area map with a suitable heuristic algorithm. Our study also reveals that there exists no deterministic polynomial time solution that can implement the desired design motives as well as the feasibility of our planned methodology. Compared to any brute force strategy, as per the simulation results, our approach shows 42% higher delivery probability and 49% lower latency.

SRSnF: A Strategy for Secured Routing in Spray and Focus Routing Protocol for DTN

CHENNAI - INDIA

Proceedings of the Second International Conference on Advances in Computing and Information Technology (ACITY 2012)

Conferences PDF S. Saha, R. Verma, S. Sengupta, V. Mishra, S. Nandi

SRSnF: A Strategy for Secured Routing in Spray and Focus Routing Protocol for DTN

S. Saha, R. Verma, S. Sengupta, V. Mishra, S. Nandi Conferences

This paper deals with the aspect of security in Delay Tolerant Networks (DTN). DTNs are characterized with decentralized control. Network performance and trustworthiness of transmitted information in DTNs depend upon the level of co-operation among participating nodes. As a result, DTNs are vulnerable towards untoward activities arising out of node selfishness as well as malicious intentions. In this paper, we limit our focus to the Black Hole Denial-of-Service attack. We develop a table-based strategy to record network history and use this information to detect discrepancies in the behavior of nodes, followed by elimination of those detected as malicious. We explain our detection mechanism considering Spray and Focus routing protocol as the representative routing scheme. The detection mechanism has been described in detail with examples pertaining to various case scenarios. Furthermore, we study the effect of variation of various parameters on detection efficiency and message transmission through simulation results.