Long-Range Low-Cost Networking for Real-Time Monitoring of Rail Tracks in Developing Countries
Derailments present a frequent phenomenon in several developing countries, which result in massive loss of property along with death tolls. A real-time automated system is needed to detect uprooted or faulty rail blocks to prevent derailments. One of the solutions in this context is to sense the vibration of the rail track having an incoming train and transmit the information to the train notifying it about the condition of the rail tracks ahead. However, existing studies in this regard are yet to present a pragmatic solution that enables much-demanded long-distance networking to transmit the sensed data. The demand for long-distance network communication between the sensor nodes and the incoming train is unavoidable, as stopping the train after sensing an uprooted or faulty rail block ahead needs a considerable response time and distance. Therefore, in this research project, we develop a low-cost, long-range, and highly reliable mobile multi-hop networking scheme to successfully transmit data sensed from rail tracks to an approaching train at a distance of around 2000m. By considering the effect of Fresnel’s Region in our study, we determine the suitable placement of the networking module on the rail track, which leads us to achieve a delivery ratio of more than 99%. We confirm this finding through rigorous experiments over a real testbed scenario enabling mobile multi-hop networking. This work is accepted in International Conference on Information & Communication Technologies and Development (ICTD), ACM, 2022
Human Survey Interaction
This project is undertaken considering the fact that mass gatherings such as Hajj, Umrah, etc., are often riddled with crowd-related problems. So quantifying the attendees' experience is crucial to have a concise understanding of the difficulties and their remedies. To that end, we conducted a mass-scale survey of Hajj and Umrah pilgrims, where we engaged paid human data collectors to conduct a substantial part of the process. After that, we devised an analysis of the integrity of the data collectors through iterative focused group discussions. This work was published in IEEE Access. Then, a thematic analysis of the responses was done which helped them identify major problems and recommendations expressed by pilgrims. We are currently working to prepare this finding for journal submission.
Network analysis of Hajj pilgrims’ experience of problems
Here, we surveyed nearly 1000 pilgrims from diverse demographics to explore problems faced by them and to reveal mutual associations between those problems. Our research showed strong associations among different problems and demographics of the pilgrims. It is found that hygienic washrooms, transportation, and getting lost are the most prevalent problems among the pilgrims. Pilgrims with less education and poor English proficiency are the ones mostly affected by these problems. This work is currently under revision in the Journal of Hospitality and Tourism Management.
Preparation of a novel and diversified Hajj crowd image data set for a real-time crowd-monitoring task
Here, I contributed through rigorous research and literature review to utilize and adopt appropriate image similarity techniques for predicting the mobility of the crowd by using images only instead of video streams. We were successful in diversifying these findings into new aspects of crowd computing such as in transportation systems. To that end, we have worked on vehicle detection on roads where the unstructured organization of traffic can be a severe inconvenience. We have focused on using minimal computational resources while doing so, as we are aware of our country's constraints in terms of calculation power and capability. This work has been published in IEEE International Conference on Data Mining(ICDM), 2021.
Towards quantifying software engineers’ reception and response to new versions of programming languages that can be found on Q&A sites i.e. Stack Overflow and GitHub
Our motivation behind this project is to identify factors that influence developers’ acceptance of a newer version of languages. We were able to investigate Stack Overflow and GitHub datasets to extract relevant topics for topic modeling. Since this finding needed to be substantiated with practical surveys with industry peers, we prepared an appropriate questionnaire that was distributed among developers of diverse demographics. Then we conducted statistical analysis to find out the correlation and validation between datasets and survey data responses. Currently, we are working towards preparing the manuscript of our research work for journal publication.