Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
3 results
Search Results
Item Remote Surveillance Robot System-A Robust Framework Using Cloud(Institute of Electrical and Electronics Engineers Inc., 2016) Sundaram, A.; Gupta, M.; Rathod, V.; Chandrasekaran, K.Today's technology provides a rather convenient way for developer community to develop an integrated network environment for the diversified applications of various robotic systems. Internet based robotic systems have been gaining importance of late and we explore one such application in the field of remote surveillance. Even though direct control has potential difficulties due to unpredictable delay, recent technological advances have reduced this to a large extent. This paper describes a direct model of robot control. The system uses standard protocol and a machine-human interface. Using a Web browser (thin client), a remote user can control the mobile robot to navigate in an enclosure with visual feedback via the Internet. The use of an intuitive user interface permits Internet users to access and control the mobile robot and perform useful tasks remotely, from a different location. The direct mode is implemented with the help of event driven methods which provide complete control over the robot. This paper proposes a model architecture for direct control and discusses an implementation and performance of a networked robot system with the help of Cloud computing. © 2015 IEEE.Item 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.Item Geometric Series based effective RTO estimation Technique for CoCoA(Elsevier B.V., 2022) Rathod, V.; Tahiliani, M.P.Constrained Application Protocol (CoAP) is a standard data transfer protocol for Internet of Things. It has an in-built support for the basic congestion control mechanism that uses fixed Retransmission Time Out (RTO) for every transmission regardless of Round Trip Time (RTT), and performs Binary Exponential Backoff (BEB) when the packets get dropped. CoAP Simple Congestion Control/Advanced (CoCoA) is an enhanced congestion control mechanism over CoAP that adapts RTO based on RTT. It maintains Strong and Weak RTO estimators and uses a Variable Backoff Factor (VBF) instead of BEB when the packets get dropped. CoCoA uses an Exponential Weighted Moving Average (EWMA) to estimate the RTO for the next transmission. The weight used in EWMA is determined on the basis of whether the RTT estimated for the recent transmission was a Strong RTT or Weak RTT. However, the weights used to estimate the RTO are fixed (0.5 for Strong and 0.25 for Weak). These fixed weights lead to slow adaptation of RTO and affect the performance of the IoT applications. In this paper, we highlight the impact of having fixed weights while estimating the RTO in CoCoA. In particular, we show that the RTO in CoCoA fails to adapt quickly when the network conditions are lossless because it uses a fixed value for Strong RTO estimation (0.5). We propose a new algorithm called Geometric Series based effective RTO estimation Technique for CoCoA (GSRTC) to adapt the weight used in EWMA for estimating Strong RTO. GSRTC is integrated into CoCoA and validated against existing mechanisms using the Cooja simulator in Contiki OS and in a real testbed FIT/IoT-LAB. Our results show that GSRTC has lower Flow Completion Times (FCT), lesser retransmissions and better network throughput. © 2022 Elsevier B.V.