Browsing by Author "Singala, S."

Now showing 1 - 3 of 3

CoCoA++: Delay gradient based congestion control for Internet of Things
(2019) Rathod, V.; Jeppu, N.; Sastry, S.; Singala, S.; Tahiliani, M.P.
In this paper, we propose a new congestion control algorithm called CoCoA++ to address the issue of network congestion in Internet of Things (IoT). Unlike the existing congestion control mechanisms that operate on instantaneous Round Trip Time (RTT) measurements in IoT, we use delay gradients to get a better measure of network congestion, and implement a probabilistic backoff to deal with congestion. We integrate the delay gradients and the probability backoff factor with Constrained Application Protocol (CoAP). The proposed algorithm is implemented and evaluated using the Cooja network simulator provided by Contiki OS. Subsequently, it is deployed and evaluated in a real testbed by using the FIT/IoT-LAB. We observe that delay gradients give a more accurate measure of congestion and the Retransmission Time Out (RTO) is reduced significantly, thereby leading to less delays and high packet sending rates. CoCoA++ being a minor improvement over the existing algorithm is easy to deploy. 2019 Elsevier B.V.
CoCoA++: Delay gradient based congestion control for Internet of Things
(Elsevier B.V., 2019) Rathod, V.; Jeppu, N.; Sastry, S.; Singala, S.; Tahiliani, M.P.
In this paper, we propose a new congestion control algorithm called CoCoA++ to address the issue of network congestion in Internet of Things (IoT). Unlike the existing congestion control mechanisms that operate on instantaneous Round Trip Time (RTT) measurements in IoT, we use delay gradients to get a better measure of network congestion, and implement a probabilistic backoff to deal with congestion. We integrate the delay gradients and the probability backoff factor with Constrained Application Protocol (CoAP). The proposed algorithm is implemented and evaluated using the Cooja network simulator provided by Contiki OS. Subsequently, it is deployed and evaluated in a real testbed by using the FIT/IoT-LAB. We observe that delay gradients give a more accurate measure of congestion and the Retransmission Time Out (RTO) is reduced significantly, thereby leading to less delays and high packet sending rates. CoCoA++ being a minor improvement over the existing algorithm is easy to deploy. © 2019 Elsevier B.V.
Modeling of Human Face Expressions and Hand Movement for Animation
(Institute of Electrical and Electronics Engineers Inc., 2018) Sangeetha, G.S.; Koolagudi, S.G.; Ramteke, P.B.; Singala, S.; Sastry, S.
Animation is the process of creating an illusion of motion by moving images rapidly in an order which minimally differ from each other. In this paper, a solution is proposed to simplify the process of animation by tracking movement of hand and facial expressions. Face detection performed using Haar-Cascade classifier whereas hand detection is achieved using Otsu's binarization and Ramer-Douglas-Peucker contour detection algorithm. Facial expression landmarks are captured from the Haarlike features. Hand movements feature points are extracted from the contour. Replay phase includes drawing the virtual object by calculating the translational factors and redrawing the virtual object in every frame during replay. The proposed approach is observed to achieve the smooth translation of face expression and hand movement and reduce the time and effort needed to make the animation. Copy Right Â© INDIACom-2018.