Faculty Publications
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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 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.Item The Dependency of Healthcare on Security: Issues and Challenges(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Kittur, L.J.; Mehra, R.; Chandavarkar, B.R.Information security and privacy in the sector of healthcare is an important issue that has to be given importance. With the growing adoption of electronic health records of patients, the need of accessing and sharing information between different healthcare professionals is also increasing. This gives rise to the attention that has to be provided for securing the information. Also the adoption of the Internet of Things in wireless body sensor networks, leads to the usage of Cloud and Fog in healthcare systems. Thus pointing towards secure methods of accessing, storing, processing of sensitive data. In this paper, an overview of different issues and challenges pertaining to the security of healthcare systems is presented. Also, the solutions to address the security concerns in the healthcare systems are also discussed. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Machine Learning Powered Autoscaling for Blockchain-Based Fog Environments(Springer Science and Business Media Deutschland GmbH, 2022) Martin, J.P.; Joseph, C.T.; Chandrasekaran, K.; Kandasamy, A.Internet-of-Things devices generate huge amount of data which further need to be processed. Fog computing provides a decentralized infrastructure for processing these huge volumes of data. Fog computing environments provide low latency and location-aware alternative to conventional cloud computing by placing the processing nodes closer to the end devices. Co-ordination among end devices can become cumbersome and complex with the increasing amount of IoT devices. Some of the major challenges faced while executing services in the fog environment is the resource provisioning for the user services, service placement among the fog devices and scaling of fog devices based on the current load on the network. Being a decentralized infrastructure, fog computing is vulnerable to external threats such as data thefts. This work presents a blockchain based fog framework for making autoscaling decisions with the use of machine learning techniques. Evaluation is done by performing a series of experiments that show how the services are handled by the fog framework and how the autoscaling decisions are made. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item A Blockchain-Enabled IoT Framework for NICU Infant Health Monitoring System(Institute of Electrical and Electronics Engineers Inc., 2023) Madhusudhan, R.; Pravisha, P.According to the World Health Organization (WHO), 15 million infants are born prematurely each year. In the neonatal intensive care unit (NICU), the critical health parameters of newborn babies must be monitored precisely and in real time. Approximately one million preterm babies suffer morbidity before the age of five due to preterm birth and complications associated with preterm delivery. The neonatal intensive care unit (NICU) requires accurate, real-time monitoring of newborn infants' vital health parameters. One of the challenges encountered by the majority of hospitals is the lack of systems that can track real-time health parameters and notify doctors and parents to indicate any neonatal critical conditions. This research article presents a framework that incorporates IoT, fog, deep learning technologies, Blockchain, and decentralized cloud for NICU newborn health monitoring. The development of the Internet of Things (IoT) and blockchain technologies provides wide opportunities for enhancing the data management of neonatal intensive care units. By integrating IoT devices comprising wearable sensors and smart monitors the system gets real-time data on vital signs like heart rate, temperature, blood oxygen levels, and breathing rate. Fog computing is used for the instantaneous analysis of critical data, and an efficient deep learning algorithm will be implemented at the fog layer to classify data into either critical or non-critical data. Since fog has limited resources, a private blockchain is used to store critical data. The critical data is stored temporarily on a private blockchain and permanently on a decentralized cloud. © 2023 IEEE.Item VMAP: Matching-based Efficient Offloading in IoT-Fog Environments with Variable Resources(IEEE Computer Society, 2023) Morey, J.V.; Satpathy, A.; Addya, S.K.Fog computing is a promising technology for critical, resource-intensive, and time-sensitive applications. In this regard, a significant challenge is generating an offloading solution that minimizes the latency, energy, and number of outages for a dense IoT-Fog environment. However, the existing solutions either focus on a single objective or mainly dedicate fixed-sized resources as virtual resource units (VRUs). Moreover, these solutions are restrictive and not comprehensive, resulting in poor performance. To overcome these challenges, this paper proposes a VMAP model addressing the lacunas above. Offloading problem is abstracted as a one-to-many matching game between two sets of entities - tasks and fog nodes (FNs) by considering both preferences. Moreover, the preferences and weights of the parameters are generated using the Analytic Hierarchy Process (AHP). Exhaustive simulations indicate that the proposed strategy outperforms the baseline algorithms, considering average task latency and energy consumption by 35% and 22.2%, respectively. Additionally, resource utilization also experiences a boost by 28.57%, and 97.98% of tasks complete their execution within the deadlines. © 2023 IEEE.Item LBA: Matching Theory Based Latency-Sensitive Binary Offloading in IoT-Fog Networks(Institute of Electrical and Electronics Engineers Inc., 2024) Soni, P.; Deshlahre, O.C.; Satpathy, A.; Addya, S.K.The Internet of Things (IoT) is growing more popular with applications like healthcare services, traffic monitoring, video streaming, smart homes, etc. These applications produce an enormous amount of data, so a realistic option in this instance is to offload computational tasks to their proximity fog nodes (FNs) instead of the remote cloud. However, a negligent offloading strategy may cause anomalous computational traffic load at the FNs, causing congestion that may adversely affect the latency. However, the latency of task flows from IoT devices comprises communications latency at BS and computational latency at FNs. Therefore, designing offloading algorithms to distribute the computational load at FN evenly and efficiently utilize the FN resources is crucial. To solve this problem, we proposed LBA in a fog network with a binary offloading strategy using the matching theory-based approach. We utilize the Analytic Hierarchy Process (AHP) to generate the preference list. Furthermore, the binary offloading technique follows the deferred acceptance algorithm (DAA) to produce a stable assignment, and the complete offloading problem is modeled as a one-to-many matching game. Comprehensive simulations ensure that LBA can accomplish a better-balanced assignment for homogeneous and heterogeneous input concerning all the baseline algorithms. © 2024 IEEE.Item Elucidating the challenges for the praxis of fog computing: An aspect-based study(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2019) Martin, J.P.; Kandasamy, A.; Chandrasekaran, K.; Joseph, C.T.The evolutionary advancements in the field of technology have led to the instigation of cloud computing. The Internet of Things paradigm stimulated the extensive use of sensors distributed across the network edges. The cloud datacenters are assigned the responsibility for processing the collected sensor data. Recently, fog computing was conceptuated as a solution for the overwhelmed narrow bandwidth. The fog acts as a complementary layer that interplays with the cloud and edge computing layers, for processing the data streams. The fog paradigm, as any distributed paradigm, has its set of inherent challenges. The fog environment necessitates the development of management platforms that effectuates the orchestration of fog entities. Owing to the plenitude of research efforts directed toward these issues in a relatively young field, there is a need to organize the different research works. In this study, we provide a compendious review of the research approaches in the domain, with special emphasis on the approaches for orchestration and propose a multilevel taxonomy to classify the existing research. The study also highlights the application realms of fog computing and delineates the open research challenges in the domain. © 2019 John Wiley & Sons, Ltd.Item Mobility aware autonomic approach for the migration of application modules in fog computing environment(Springer Science and Business Media Deutschland GmbH, 2020) Martin, J.P.; Kandasamy, A.; Chandrasekaran, K.The fog computing paradigm has emanated as a widespread computing technology to support the execution of the internet of things applications. The paradigm introduces a distributed, hierarchical layer of nodes collaboratively working together as the Fog layer. User devices connected to Fog nodes are often non-stationary. The location-aware attribute of Fog computing, deems it necessary to provide uninterrupted services to the users, irrespective of their locations. Migration of user application modules among the Fog nodes is an efficient solution to tackle this issue. In this paper, an autonomic framework MAMF, is proposed to perform migrations of containers running user modules, while satisfying the Quality of Service requirements. The hybrid framework employing MAPE loop concepts and Genetic Algorithm, addresses the migration of containers in the Fog environment, while ensuring application delivery deadlines. The approach uses the pre-determined value of user location for the next time instant, to initiate the migration process. The framework was modelled and evaluated in iFogSim toolkit. The re-allocation problem was also mathematically modelled as an Integer Linear Programming problem. Experimental results indicate that the approach offers an improvement in terms of network usage, execution cost and request execution delay, over the existing approaches. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.Item CREW: Cost and Reliability aware Eagle-Whale optimiser for service placement in Fog(John Wiley and Sons Ltd cs-journals@wiley.co.uk, 2020) Paul Martin, J.; Kandasamy, A.; Chandrasekaran, K.Integration of Internet of Things (IoT) with industries revamps the traditional ways in which industries work. Fog computing extends Cloud services to the vicinity of end users. Fog reduces delays induced by communication with the distant clouds in IoT environments. The resource constrained nature of Fog computing nodes demands an efficient placement policy for deploying applications, or their services. The distributed and heterogeneous features of Fog environments deem it imperative to consider the reliability performance parameter in placement decisions to provide services without interruptions. Increasing reliability leads to an increase in the cost. In this article, we propose a service placement policy which addresses the conflicting criteria of service reliability and monetary cost. A multiobjective optimisation problem is formulated and a novel placement policy, Cost and Reliability-aware Eagle-Whale (CREW), is proposed to provide placement decisions ensuring timely service responses. Considering the exponentially large solution space, CREW adopts Eagle strategy based multi-Whale optimisation for taking placement decisions. We have considered real time microservice applications for validating our approaches, and CREW has been experimentally shown to outperform the existing popular multiobjective meta-heuristics such as NSGA-II and MOWOA based placement strategies. © 2020 John Wiley & Sons Ltd