Scope
We have stepped into an era of edge computing where analytics applications process huge volumes of data generated by IoT devices to provide insights, answers to complex questions, and improved performance. Most applications that run on the edge such as smart health monitoring, agro-analytics, autonomous vehicle control, etc., use a cluster of edge devices to perform a wide range of tasks from machine learning based prediction to report generation. Every physical device has the capability to connect to the Internet (digital presence) to send and receive data. Internet-connected cars, home automation, and cameras are all examples of the interconnected Internet of Things (IoT). As wireless networks connect the systems in an IoT platform and the devices are resource constrained, they are subject to a wide variety of vulnerabilities, almost at each layer of the network architecture. Heterogeneity and high resource constraints of IoT devices (in terms of memory, energy, and computing capacity) make development of efficient, lightweight edge analytics solutions a real challenging task. As a trending use case of edge computing, UAVs are being universally adopted as the computation infrastructure for running analytics, tracking, planning, and control activities in a wide range of domains such as traffic management, disaster management, agriculture, surveillance, etc. Important properties of a multi-UAV system are robustness, adaptivity, resource efficiency, scalability, cooperativeness, heterogeneity, and self-configurability. If communication infrastructure is lacking, the use of UAVs as relays between disconnected ground stations will become imperative. Specifically, the recent global-scale COVID19 pandemic additionally enforces the need to control and ensure social distancing in fields, so that there is minimal contact and disease spread. Drones, coupled with imaging and state-of-the-art AI and computer vision technologies can be used to optimally schedule human involvement while ensuring adequate social distancing is maintained. Drone sorites scheduled periodically over target fields can gather field data which can be used to assess crop requirements and address them. Further, drone-based analytics can be used to ensure that the farmers perform the tasks maintaining social distancing norms.
By bringing together academic and industrial representatives from each of these individual domains, this workshop will provide a platform to gain from for those who are either already working in this field or waiting for the right inputs to enter the field.
The wide area for the contributions would be on MEC, specifically covering topics on Industrial IoT, Cellular-V2X, Tele-operations (Haptics), Drone Computing/Analytics, Mobile Computing.
Workshop Organizers:
- Subhajit Siddhanta, Assistant Professor, IIT Bhilai
- Arzad A Kherani, Associate Professor, IIT Bhilai
