ProgramsView all programs
From the news
- Chancellor Amma Addresses the Parliament of World’s Religions
- Amrita Students Qualify for the European Mars Rover Challenge
Publication Type : Conference Proceedings
Thematic Areas : Amrita Center for Cybersecurity Systems and Networks
Publisher : IEEE International Conference on Advanced Networks and Telecommunication Systems (ANTS), IEEE, Bangalore, India .
Source : IEEE International Conference on Advanced Networks and Telecommunication Systems (ANTS), IEEE, Bangalore, India (2016)
Url : https://www.scopus.com/record/display.uri?eid=2-s2.0-85021875141&origin=resultslist&sort=plf-f&src=s&st1=Modeling+the+Performance+of+IoT+Networks&st2=&sid=351319744A648BAF6E025864B421BDDC.wsnAw8kcdt7IPYLO0V48gA%3a20&sot=b&sdt=b&sl=55&s=TITLE-ABS-KEY%28Mod
Keywords : Analytical models, application-aware performance tuning, computational modeling, decision making, decision making capabilities, individual node behavior, Internet of things, IoT networks, Logic gates, Markov chains, Markov processes, optimal sleep-wake-up schedules, Predictive models, Protocols, real-time communication, Real-time systems, Sensors, simulation based models, steady state transition probabilities, Throughput
Campus : Amritapuri
School : Centre for Cybersecurity Systems and Networks, Department of Computer Science and Engineering
Center : Cyber Security
Department : cyber Security
Year : 2016
Abstract : Internet of Things (IoTs) is gaining increasing significance due to real-time communication and decision making capabilities of sensors integrated into everyday objects. Predicting performance in IoTs is critical for detecting performance bottlenecks, designing optimal sleep/wake-up schedules and application-aware performance tuning. However, performance prediction becomes a significant challenge in IoTs due to varying needs of applications coupled with the resource constrained nature of sensors. In this work, we analyze the impact of factors affecting performance in IoT networks using simulation based models. Further, an analytical framework is developed to model the impact of individual node behavior on overall performance using Markov chains. In particular, we derive steady state transition probabilities of transmit and receive states using protocol execution traces and further utilize them towards predicting per-flow throughput. Our proposed model is generic in that it can be applied across domains. Accuracy of the model is evaluated by comparing the predictions with the actual estimates obtained using simulations.
Cite this Research Publication : S. Sankaran, “Modeling the Performance of IoT Networks”, IEEE International Conference on Advanced Networks and Telecommunication Systems (ANTS). IEEE, Bangalore, India, 2016.