Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
10 results
Search Results
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 LtdItem Adopting elitism-based Genetic Algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment(Academic Press, 2021) Natesha, B.V.; Guddeti, R.M.R.Fog computing is an emerging computation technology for handling and processing the data from IoT devices. The devices such as the router, smart gateways, or micro-data centers are used as the fog nodes to host and service the IoT applications. However, the primary challenge in fog computing is to find the suitable nodes to deploy and run the IoT application services as these devices are geographically distributed and have limited computational resources. In this paper, we design the two-level resource provisioning fog framework using docker and containers and formulate the service placement problem in fog computing environment as a multi-objective optimization problem for minimizing the service time, cost, energy consumption and thus ensuring the QoS of IoT applications. We solved the said multi-objective problem using the Elitism-based Genetic Algorithm (EGA). The proposed approach is evaluated on fog computing testbed developed using docker and containers on 1.4 GHz 64-bit quad-core processor devices. The experimental results demonstrate that the proposed method outperforms other state-of-the-art service placement strategies considered for performance evaluation in terms of service cost, energy consumption, and service time. © 2021 Elsevier LtdItem Bit error rate analysis of polarization shift keying based free space optical link over different weather conditions for inter unmanned aerial vehicles communications(Springer, 2021) Nallagonda, V.; Krishnan, P.The increasing availability of unmanned aerial vehicles (UAVs) is an exciting part of future emerging technology with advanced scientific and industrial interests. Free space optical (FSO) communications’ ability to offer very high data rates and the mobility of unmanned aerial vehicle (UAV) flying platforms make the delivery of Fifth-Generation (5G) wireless networking services appealing to FSO-UAV-based solutions. UAVs play a greater role in end-to-end delivery in next- generation wireless networking systems, serving as a base station, capacity enhancement, high data access, and other disaster management systems. To establish a link between unmanned aerial vehicles and ground stations, FSO can be applied. But, the different weather conditions liken rain, fog effects on the performance of the FSO link, contributing to the loss of the signal. In this paper, we proposed polarization shift keying (POLSK) modulated FSO link based UAV–UAV communication system for 6G beyond applications. We examine the effect of different weather conditions such as rain, fog on the bit error rate (BER) performance of the proposed system. Novel closed-form expressions for UAV–UAV based FSO propagation channel are derived, and BER performance is investigated under different weather conditions. Fog and rain are the main limiting factors mitigated in this paper by suitable mitigation techniques by increasing receiver field of view. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Fog-Based Intelligent Machine Malfunction Monitoring System for Industry 4.0(IEEE Computer Society, 2021) Natesha, B.V.; Guddeti, R.M.R.There is an exponential increase in the use of Industrial Internet of Things (IIoT) devices for controlling and monitoring the machines in an automated manufacturing industry. Different temperature sensors, pressure sensors, audio sensors, and camera devices are used as IIoT devices for pipeline monitoring and machine operation control in the industrial environment. But, monitoring and identifying the machine malfunction in an industrial environment is a challenging task. In this article, we consider machines fault diagnosis based on their operating sound using the fog computing architecture in the industrial environment. The different computing units, such as industrial controller units or micro data center are used as the fog server in the industrial environment to analyze and classify the machine sounds as normal and abnormal. The linear prediction coefficients and Mel-frequency cepstral coefficients are extracted from the machine sound to develop and deploy supervised machine learning (ML) models on the fog server to monitor and identify the malfunctioning machines based on the operating sound. The experimental results show the performance of ML models for the machines sound recorded with different signal-to-noise ratio levels for normal and abnormal operations. © 2021 IEEE.Item Decentralised priority-based shortest job first queue model for IoT gateways in fog computing(Inderscience Publishers, 2022) Jayashree, N.; Babu, B.S.; Talawar, B.An increased growth in time-critical IoT applications, led to a rise in real-time resource requirements. The stringent deadlines on latency have made IoT applications move out from far away cloud servers to distributed fog computing devices infrastructure which is available locally. To meet the touchstones of deadlines and processing times, there is a need to prioritise the job scheduling through the IoT gateways to appropriate fog devices. Studies showed that the queuing models exhibit uncertainties in choosing suitable computing devices, applying priorities to the jobs, deadline achievements, and minimum latency constraints. In this paper, we propose a Decentralised Priority-based Shortest Job First (DPSJF) queuing model for the IoT gateways for a fog computing infrastructure, which uses the priority-based jobs sorting technique to achieve better performance and also overcome most of the uncertainties in queuing. © © 2022 Inderscience Enterprises Ltd.Item Containerized deployment of micro-services in fog devices: a reinforcement learning-based approach(Springer, 2022) Nath, S.B.; Chattopadhyay, S.; Karmakar, R.; Addya, S.K.; Chakraborty, S.; Ghosh, S.K.The real power of fog computing comes when deployed under a smart environment, where the raw data sensed by the Internet of Things (IoT) devices should not cross the data boundary to preserve the privacy of the environment, yet a fast computation and the processing of the data is required. Devices like home network gateway, WiFi access points or core network switches can work as a fog device in such scenarios as its computing resources can be leveraged by the applications for data processing. However, these devices have their primary workload (like packet forwarding in a router/switch) that is time-varying and often generates spikes in the resource demand when bandwidth-hungry end-user applications, are started. In this paper, we propose pick–test–choose, a dynamic micro-service deployment and execution model that considers such time-varying primary workloads and workload spikes in the fog nodes. The proposed mechanism uses a reinforcement learning mechanism, Bayesian optimization, to decide the target fog node for an application micro-service based on its prior observation of the system’s states. We implement PTC in a testbed setup and evaluate its performance. We observe that PTC performs better than four other baseline models for micro-service offloading in a fog computing framework. In the experiment with an optical character recognition service, the proposed PTC gives average response time in the range of 9.71 sec–50 sec, which is better than Foglets (24.21 sec–80.35 sec), first-fit (16.74 sec–88 sec), best-fit (11.48 sec–57.39 sec) and mobility-based method (12 sec–53 sec). A further scalability study with an emulated setup over Amazon EC2 further confirms the superiority of PTC over other baselines. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Fog Assisted Personalized Dynamic Pricing for Smartgrid(Institute of Electrical and Electronics Engineers Inc., 2023) Joseph, C.T.; Martin, J.P.; Chandrasekaran, K.; Raja, S.P.Unit electricity pricing is of vital importance in an electric grid network. It is essential to charge the customers in a fair manner. Traditional pricing models are found to be inadequate in the ability to charge customers fairly due to a lack of support for real-time communication between customers and electricity providers. With the introduction of smart devices in the electric grid domain, the real-time gathering of information is a seamless process. Such an electric network that uses smart devices is called a smart grid. In a smart grid network, electricity providers can monitor the electricity usage pattern of customers in a real-time manner, which can then be analyzed to determine the appropriate prices. To analyze the customer's history of usage and price the electricity in a real-time manner, the computation must be performed with minimal latencies. Adoption of a fog computing layer in the smart grids can aid in the attainment of this goal. In this article, we propose a novel method for the pricing of electricity. In our approach, the electric demand of a household is predicted based on their past usage patterns. Users are then clustered into different bins based on their demands, and an evolutionary algorithm is used to generate the prices for the users present in different bins in a real-time manner to ensure the maximum attainable profit to a service provider. © 2014 IEEE.Item Delay-aware partial task offloading using multicriteria decision model in IoT–fog–cloud networks(Academic Press, 2025) S.a, S.; E, M.; Addya, S.K.; Rahman, S.; Pal, S.; Karmakar, C.Fog computing plays a prominent role in offloading computational tasks in heterogeneous environments since it provides less service delay than traditional cloud computing. The Internet of Things (IoT) devices cannot handle complex tasks due to less battery power, storage and computational capability. Full offloading has issues in providing efficient computation delay due to more response time and transmission cost. A suitable solution to overcome this problem is to partition the tasks into splittable subtasks. Considering multi-criteria decision parameters like processing efficiency and deadline helps to achieve efficient resource allocation and task assignment. The matching theory is applied to map task nodes to heterogeneous fog nodes and VMs for stability. Compared to baseline algorithms, proposed algorithms like Resource Allocation based on Processing Efficiency (RABP) and Task Assignment Based on Completion Time (TAC) are efficient enough to provide reasonable service delay and discard the non-beneficial tasks, i.e., tasks that do not execute within the deadline. © 2025 The Authors