K.E. Srinivasa Desikan
@iiitk.ac.in
Assistant Professor, Department of Computer Science and Engineering
Indian Institute of Information Technology Kurnool
RESEARCH INTERESTS
Internet of Things, Fog Computing, Edge Computing, Wireless Networks
Scopus Publications
Scopus Publications
K E Srinivasa Desikan, Vijeth J Kotagi, and C Siva Ram Murthy
Institute of Electrical and Electronics Engineers (IEEE)
K.E. Srinivasa Desikan, Vijeth J. Kotagi, and C. Siva Ram Murthy
Elsevier BV
K E Srinivasa Desikan, Vijeth J Kotagi, and C Siva Ram Murthy
IEEE
A smart city aims at providing effective services to its citizens and administrators through the Internet of things (IoT) paradigm. A typical smart city architecture consists of a large topology with heterogeneous data generating sensors and communication technologies with support for multiple applications such as traffic management, smart energy, smart homes, and industrial IoT. Each application has a unique data generation rate and latency constraint based on the criticality of the application. In traditional IoT architecture, the data is processed in the Cloud which results in high propagation delay and hence high latency. To alleviate this problem, a decentralized and distributed paradigm called Fog Computing (FC) is gaining importance in latency critical IoT networks. The Fog layer is an intelligent layer in which the intermediate gateways are smart and have processing and storage resources. Smart processing of data at the fog layer results in significant reduction in latency. However, the smartness of gateways comes with a price. Therefore, placement of these resourceful gateways has to be done in a cost efficient manner considering latency constraints of all the applications supported in the system. In this paper, we propose a Hungarian method based Topology Construction (HTC) algorithm to identify the optimal locations and resources for the gateways such that over all system cost is minimized and latency constraints are met. We perform exhaustive simulations to show the effectiveness of our algorithm by comparing with existing models.
K. E. Srinivasa Desikan, Manikantan Srinivasan, and C. Siva Ram Murthy
ACM
In generic Internet of Things (IoT) architecture, all the data generated is sent via gateways (GWs) and is processed in Cloud. This approach limits many real-time applications due to high propagation latency to the Cloud and also under-utilizes the GWs' compute and storage resources. Fog Computing (FC) extends the computability and storage of Cloud computing paradigm to network's edge devices such as GWs. IoT networks with FC enabled GWs, mitigate the latency and underutilization problems by processing data at the GWs. We propose a distributed latency-aware data processing (DLA-DP) model by which FC enabled GWs dynamically exchange processing and storage capability information and probabilistically forward data to its neighboring GWs or to Cloud only when there is a limit in local processing or storage. Modeled as a network of M/M/m/B queuing systems, a DLA-DP enabled IoT network is validated with extensive simulations. DLA-DP model enables improvements such as reduced system response time, increased gateway processing and buffer occupancy efficiencies.