Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
31 results
Search Results
Item Operating systems for IoT devices: A critical survey(Institute of Electrical and Electronics Engineers Inc., 2015) Gaur, P.; Tahiliani, M.P.The future of communication resides in Internet of Things, which is certainly the most sought after technology today. The applications of IoT are diverse, and range from ordinary voice recognition to critical space programmes. Recently, a lot of efforts have been made to design operating systems for IoT devices because neither traditional Windows/Unix, nor the existing Real Time Operating Systems are able to meet the demands of heterogeneous IoT applications. This paper presents a survey of operating systems that have been designed so far for IoT devices and also outlines a generic framework that brings out the essential features desired in an OS tailored for IoT devices. © 2015 IEEE.Item Efficient key management in IoT using mobile aggregator(Springer Verlag service@springer.de, 2016) Saurabh, S.; Pais, A.R.; Chatterjee, S.Managing keys in Internet of Things (IoT) is challenging. With this proposed work we are trying to address an efficient key management protocol for specific application based scenario which enforces secure connectivity of devices and minimizes node capture attacks. There are a number of protocols that have been enforced and implemented for wireless sensor networks (WSN) and internet-enabled devices. We propose a protocol with mobility interface using combinatorial designs for key management in IoT devices. Mobile devices follow a dedicated path to collect data securely from installed devices in the network. We also compare our work with existing protocols and few mobility models. © Springer Nature Singapore Pte Ltd. 2016.Item Survey on Optimization in IoT(Institute of Electrical and Electronics Engineers Inc., 2018) Chenna Keshava, B.S.; Srinivasan, C.K.This paper gives a brief overview of the existing literature on Optimization in Internet of Things (IoT), which is a very critical technology in the 21stcentury. A few of the papers deal with the evolution of IoT as a technology in the past 20 years, whereas a majority of the papers deal with the challenges faced in the communication, modelling and deployment of IoT applications, few of which use optimization and which are exploding in their numbers and the diversity of the type of devices. © 2018 IEEE.Item Resource Provisioning Framework for IoT Applications in Fog Computing Environment(IEEE Computer Society help@computer.org, 2018) Rakshith, G.; Rahul, M.V.; Sanjay, G.S.; Natesha, B.V.; Guddeti, R.M.The increasing utility of ubiquitous computing and dramatic shifts in the domain of Internet of Things (IoT) have generated the need to devise methods to enable the efficient storage and retrieval of data. Fog computing is the de facto paradigm most suitable to make efficient use of the edge devices and thus shifting the impetus from a centralized cloud environment to a decentralized computing paradigm. By utilizing fog resources near to the edge of the network, we can reduce the latency and the overheads involved in the processing of the data by deploying the required services on them. In this paper, we present resource provisioning framework which provisions the resources and also manages the registered services in a dynamic topology of the fog architecture. The results demonstrate that using fog computing for deploying services reduces the total service time. © 2018 IEEE.Item GA-PSO: Service Allocation in Fog Computing Environment Using Hybrid Bio-Inspired Algorithm(Institute of Electrical and Electronics Engineers Inc., 2019) Yadav, V.; Natesha, B.V.; Guddeti, R.M.R.Internet of Thing (IoT) applications require an efficient platform for processing big data. Different computing techniques such as Cloud, Edge, and Fog are used for processing big data. The main challenge in the fog computing environment is to minimize both energy consumption and makespan for services. The service allocation techniques on a set of virtual machines (VMs) is the decidable factor for energy consumption and latency in fog servers. Hence, the service allocation in fog environment is referred to as NP-hard problem. In this work, we developed a hybrid algorithm using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) technique to solve this NP-hard problem. The proposed GA-PSO is used for optimal allocation of services while minimizing the total makespan, energy consumption for IoT applications in the fog computing environment. We implemented the proposed GA-PSO using customized C simulator, and the results demonstrate that the proposed GA-PSO outperforms both GA and PSO techniques when applied individually. © 2019 IEEE.Item Security issues and challenges in Healthcare Automated Devices(Institute of Electrical and Electronics Engineers Inc., 2020) Jangid, A.; Dubey, P.K.; Chandavarkar, B.R.Automated devices can be seen everywhere be at home, office, medical devices, Mobiles, etc. This paper presents some of the healthcare-related automated devices with their shortcomings related to security. We are addicted to automated devices and in near future, we will be watching new emergence of devices with the increasing power of automation devices and their security is a big concern as the credibility of a machine is questionable and it's related to automated devices and we are left with many challenges to resolve those security threats. This paper reviews the automation devices primarily in the healthcare field and their security-related issues along with the challenges that we might face in the future while using them. Some already available solutions are presented to try to come up with possible new solutions. © 2020 IEEE.Item Hardcoded Credentials and Insecure Data Transfer in IoT: National and International Status(Institute of Electrical and Electronics Engineers Inc., 2020) Chandavarkar, B.R.Internet of Things (IoT) or Internet of Objects (IoO) is one of the emerging areas of accessing any device or object over the internet anytime, anywhere. The limited power, memory, and processing capabilities of these tiny devices result in many of the challenging issues such as connectivity, performance and scaling, mobility, interoperability/standards, security, and privacy. The essential requirement in the successful deployment of IoT is either a new way of addressing the issue or the lightweight solution of the existing approaches. This paper majorly focuses on the security aspects of IoT. Specifically, hardcoded or weak guessable credentials and insecure data transfer related security issues in IoT. Further, the national and international status of these two security issues followed by the mitigation approaches. Finally, the importance of the paper in the context of the current status of the Indian IoT market. © 2020 IEEE.Item Deep Learning Based Smart Garbage Monitoring System(Institute of Electrical and Electronics Engineers Inc., 2020) Rao, P.P.; Rao, S.P.; Ranjan, R.India has witnessed an unprecedented increase in garbage levels in the past 20 years. Massive quantities of waste, particularly solid waste, are generated daily and seldom picked up. Consequently, garbage is being dumped in landfills and water bodies, hence not managed effectively. This mismanagement has detrimental consequences on our environment. Thus, there is a need to develop an efficient system to manage waste. In this paper, an IoT-based, automated smart bin monitoring system is proposed. Moreover, a deep learning model was used to forecast future garbage levels from the data collected. The proposed neural network model was able to predict garbage levels with an accuracy of 80.33%. Results verify the accurate prognosis of garbage levels. Additionally, data were analysed using bar charts. The amalgamation of IoT and Deep learning can bring a revolutionary change in technology and be applied to waste management. Consequently, prediction and examination of garbage levels may help municipal authorities incorporate an efficient garbage management system and reduce the overflow of garbagebins. © 2020 IEEE.Item Geometric Sequence Technique for Effective RTO Estimation in CoAP(IEEE Computer Society, 2020) Rathod, V.J.; Tahiliani, M.P.Internet of Things (IoT) is a network where physical objects with Internet connectivity can interact and exchange information with other connected objects. IoT devices are constrained in terms of power and memory, and have limited communication capabilities. The Constrained Application Protocol (CoAP) is a lightweight messaging protocol which is widely used by various IoT applications in low power and lossy wireless networks. CoAP provides reliability and minimal congestion control via a fixed Retransmission TimeOut (RTO) and Binary Exponential Backoff (BEB). It does not maintain end-to-end connection information and therefore, cannot adapt RTO based on the network conditions. Moreover, CoAP resets the RTO to its default value after having received the ACK for the retransmitted packet. This approach of resetting the RTO degrades the performance in a network with high latency and leads to spurious retransmissions. In this paper, we propose a Geometric Sequence Technique (GST) for effective RTO estimation in CoAP. GST retains the previous RTO value after having received the ACK for the retransmitted packet and eventually returns to the default value by decreasing the RTO depending on the number of consecutive successful transmissions. The proposed technique is implemented in Contiki OS and validated against the existing mechanisms. The experiments have been conducted using the Cooja simulator and the FIT/IoT-LAB testbed to verify the effectiveness of the proposed technique. The results show that GST minimizes the Flow Completion Times (FCT), reduces the number retransmissions and improves the network throughput. © 2020 IEEE.Item Fog-Based Video Surveillance System for Smart City Applications(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Natesha, B.V.; Guddeti, G.R.M.With the rapid growth in the use of IoT devices in monitoring and surveillance environment, the amount of data generated by these devices is increased exponentially. There is a need for efficient computing architecture to push the intelligence and data processing close to the data source nodes. Fog computing will help us to process and analyze the video at the edge of the network and thus reduces the service latency and network congestion. In this paper, we develop fog computing infrastructure which uses the deep learning models to process the video feed generated by the surveillance cameras. The preliminary experimental results show that using different deep learning models (DNN and SNN) at the different levels of fog infrastructure helps to process the video and classify the vehicle in real time and thus service the delay-sensitive applications. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.