Kolichala Rajashekar

Indian Institute of Technology Bhilai

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Subhajit Sidhanta

Indian Institute of Technology Bhilai

Souradyuti Paul

Indian Institute of Technology Bhilai

Sushanta Karmakar

Indian Institute of Technology Guwahati

Cooperative Institutions

Indian Institute of Technology Bhilai

Indian Institute of Technology Kharagpur

Indian Institute of Engineering Science and Technology, Shibpur

Indian Institute of Technology Jodhpur

Louisiana State University

Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento

University of Lisbon

Louisiana State University Agricultural Center

Indian Institute of Technology Gandhinagar

Indian Institute of Technology Indore

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Towards a Mobility-cum-Battery Aware Dynamic UAV Deployment for Uninterrupted Connectivity

Kolichala Rajashekar Subhajit Sidhanta Souradyuti Paul

10.1109/noms59830.2024.10575209

Cite

Citations (0)

Topology Aware Cluster Configuration for Minimizing Communication Delay in Edge Computing

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) (2022)

Kolichala Rajashekar Souradyuti Paul Sushanta Karmakar Subhajit Sidhanta

For real-time edge computing applications working under stringent deadlines, communication delay between IoT devices and edge devices needs to be minimized. Since the generalized assignment problem being NP-Hard, an optimal assignment of IoT devices to the edge cluster is hard. We propose the application RL based heuristics to obtain a near-optimal assignment of IoT devices to the edge cluster while ensuring that none of the edge devices are overloaded. We demonstrate that our algorithm outperforms the state-of-the-art.

Heuristics

Edge device

10.1109/icdcs54860.2022.00144

Cite

Citations (3)

Reinforcement Learning for Minimizing Communication Delay in Edge Computing

2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) (2022)

Kolichala Rajashekar

For real-time edge computing applications working under stringent deadlines, communication delay between IoT devices and edge devices needs to be minimized. In order to minimize the communication delay between the IoT devices and the edge devices, we need a sophisticated approach for assignment IoT devices to the edge devices. Most of the heuristics solutions previously used to tackle the problem faced issues being solution stuck at local optima and high computational over head. To that end, researchers used reinforcement learning (RL) algorithms to explore the search space to get near optimal solutions. For our work, we consider RL based algorithms and show the preliminary results.

Heuristics

Edge device

10.1109/icdcs54860.2022.00128

Cite

Citations (2)

Minimizing Data Retrieval Delay in Edge Computing

Kolichala Rajashekar Souradyuti Paul Sushanta Karmakar Subhajit Sidhanta

10.1007/978-3-031-63992-0_5

Cite

Citations (1)

Reinforcement Learning for Real-Time Federated Learning for Resource-Constrained Edge Cluster

Journal of Network and Systems Management (2024)

Kolichala Rajashekar Souradyuti Paul Sushanta Karmakar Subhajit Sidhanta

Edge device

10.1007/s10922-024-09857-1

Cite

Citations (0)

Reinforcement Learning for Real-Time Multi-Access Edge Computing

Kolichala Rajashekar

We examine data-intensive real-time applications, such as forest fire detection, medical emergency services, oil pipeline monitoring, etc., that require relatively low response time in processing data from the Internet of Things (IoT) devices. Typically, in such circumstances, the edge computing paradigm is utilised to drastically reduce the processing delay of such applications. However, with the growing IoT devices, the edge device cluster needs to be configured properly such that the real-time requirements are met. Therefore, the cluster configuration must be dynamically adapted to the changing network topology of the edge cluster in order to minimise the observed overall communication delay incurred by edge devices when processing data from IoT devices. To this end, we propose an intelligent assignment of IoT devices to edge devices based on Reinforcement Learning such that communication delay is minimised and none of the edge devices is overloaded. We demonstrate, with some preliminary results, that our algorithm outperforms the state-of-the-art.

Edge device

Response time

10.1145/3571306.3571358

Cite

Citations (0)

Mobility Aware Edge Computing: Challenges and Opportunities in Real-Time Applications

International Journal of Engineering Research and (2021)

Kolichala Rajashekar

Source

Cite

Citations (0)

Topology-Aware Cluster Configuration for Real-time Multi-access Edge Computing

Kolichala Rajashekar Sushanta Karmakar Souradyuti Paul Subhajit Sidhanta

We consider data-intensive real-time systems, such as mission-critical data-intensive applications such as forest fire detection, medical emergency services, oil pipeline monitoring, etc., which demand relatively low response time in processing data from IoT (Internet of Things) devices. Usually, in such cases, the edge computing paradigm is leveraged to drastically reduce the processing delay of such applications by performing the computations on edge devices placed closer to the data sources, i.e., the IoT devices. However, most edge devices, such as cellular phones, tablets, and UAVs (Unmanned Aerial Vehicles), are mobile in nature. Hence, the cluster configuration must be dynamically adapted with respect to the changing network topology of the edge cluster such that the observed overall communication delay incurred by the edge devices in processing the data from the IoT devices is minimized. To that end, we propose Deep Reinforcement Learning-based intelligent assignment of IoT devices to non-stationary edge devices such that the communication delay is minimized and none of the edge devices is overloaded. We demonstrate, with some preliminary results, that our algorithm outperforms the state-of-the-art.

Edge device

10.1145/3571306.3571417

Cite

Citations (2)