Edge Computing
Cloud Computing

To utilize the unused computational resources of edge/fog devices (in the internet of things) to provide better quality of service to users connected to them.

Team Members

  • Vinay Chamola (Senior Member, IEEE)
  • G S S Chalapathi
  • Vikas Hassija


  • Rajkumar Buyya, Univ. of Melbourne, Australia (Fellow, IEEE)
  • Nirwan Ansari, NJIT, USA (Fellow, IEEE)
  • Tham Chen Khong, National University of Singapore, Singapore.

Our publications in Edge Computing

Over the past decade, the Internet of Things (IoT) has attracted enormous interest from the research community and industry. IoT requires a synergy of various technologies, and Wireless Sensor Networks (WSNs) are poised to play a critical role in many IoT applications like weather monitoring, smart-grid, smart-city, etc. Synchronization of local clocks of the WSN nodes is essential in many network functionalities and thus a time synchronization protocol is required in WSNs. Although several synchronization protocols have been proposed for WSNs, most of them are simulation-based works. They make many assumptions at a high abstraction level and do not take into account the conditions of the Line-of-Sight (LOS) in the network. These factors significantly affect the performance of these protocols. Thus, conclusive experimental proof of the effectiveness of these protocols for different LOS conditions is required. In this direction, this work proposes a time synchronization protocol called Efficient and Simple Algorithm for Time Synchronization (E-SATS) for a cluster-based WSN. E-SATS has been tested on a large-sized WSN testbed in different LOS scenarios in this work and compared with the existing state-of-the-art protocols. E-SATS outperformed existing protocols by achieving up to 6 times better accuracy as compared to existing protocols with significantly lesser computations and energy consumption.

Over the past decade, there has been an increasing demand for mobile devices to perform computationally intensive tasks. However, the computational capability of these devices is limited due to memory, power and portability constraints. One of the feasible and attractive ways to enhance the performance of the resource-limited mobile devices is to offload their computationally intensive tasks on to the cloud servers when internet connectivity is available. However, when cloud servers are involved in processing, the latency and cost of computation increases. To mitigate these problems, devices with high computational resources, called cloudlets, can be deployed in the locations close to the mobile users/devices. The mobile devices can then offload their computationally intensive tasks on to them. Due to easier access and nearness of the cloudlets, the cost and latency in processing the tasks decreases. In this work, we focus on task assignment problem in a multi-cloudlet network connected via a wireless SDN network, which services the task offload requests from mobile devices in a given locality. The aim of the proposed solution is to minimize latency and thus enhance the quality of service for mobile devices. We prove the optimality of the proposed solution mathematically and employ an admission control policy to maintain this optimality even in heavily loaded networks. We also perform numerical simulations for two scenarios of small and large networks and evaluate the performance for varying traffic and network parameters. The results demonstrate that the proposed task assignment method offers reduced latency compared to state-of-the-art task assignment approaches and hence improves the quality of service offered to mobile devices.


The unprecedented outbreak of the 2019 novel coronavirus, termed as COVID-19 by the World Health Organization (WHO), has placed numerous governments around the world in a precarious position. The impact of the COVID-19 outbreak, earlier witnessed by the citizens of China alone, has now become a matter of grave concern for virtually every country in the world. The scarcity of resources to endure the COVID-19 outbreak combined with the fear of overburdened healthcare systems has forced a majority of these countries into a state of partial or complete lockdown. The number of laboratory-confirmed coronavirus cases has been increasing at an alarming rate throughout the world, with reportedly more than 3 million confirmed cases as of 30 April 2020. Adding to these woes, numerous false reports, misinformation, and unsolicited fears in regards to coronavirus, are being circulated regularly since the outbreak of the COVID-19. In response to such acts, we draw on various reliable sources to present a detailed review of all the major aspects associated with the COVID-19 pandemic. In addition to the direct health implications associated with the outbreak of COVID-19, this study highlights its impact on the global economy. In drawing things to a close, we explore the use of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), blockchain, Artificial Intelligence (AI), and 5G, among others, to help mitigate the impact of COVID-19 outbreak.