Quantum and blockchain based <scp>Serverless</scp> edge computing: A vision, model, new trends and future directions

Gill, Sukhpal Singh

doi:10.1002/itl2.275

Cited by 57 publications

(32 citation statements)

References 29 publications

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“…The possible future directions can be: DEEDSP can utilize latest artificial intelligence or machine learning models to optimize the QoS parameters 12 DEEDSP can incorporate security by utilizing the concept of blockchain or quantum computing, which can further increase the computational power 13 DEEDSP can be utilized for other IoT applications such as Industry 4.0, healthcare or agriculture 12 …”

Section: Discussionmentioning

confidence: 99%

“…DEEDSP can be utilized for other IoT applications such as Industry 4.0, healthcare or agriculture 12 DEEDSP can use the concept of serverless edge computing to scale the applications effectively 13 …”

Section: Discussionmentioning

confidence: 99%

“…The energy conservation of the fog computing system is also important and must be taken into consideration. 13 On the other hand, it is equally important to guarantee the QoS such as response time of the application within the given deadline. 14 We systematically inquire the trade-off between the power consumption and response delay in the fog computing system.…”

Section: Motivation and Our Contributionsmentioning

confidence: 99%

“…The massive scale and rising demands for service, raise the power usage on cloud servers. The energy conservation of the fog computing system is also important and must be taken into consideration 13 . On the other hand, it is equally important to guarantee the QoS such as response time of the application within the given deadline 14 .…”

Section: Introductionmentioning

confidence: 99%

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DEEDSP: Deadline‐aware and energy‐efficient dynamic service placement in integrated Internet of Things and fog computing environments

Raghavendra

Chawla

Gill

2021

Trans Emerging Tel Tech

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Fog computing has become adaptable and also as a promising infrastructure for providing elastic resources at the edge of the network. Fog computing reduces the transmission latency and consumption of bandwidth while processing the incoming requests from various Internet of Things (IoT) devices.Moreover, fog computing can support and facilitate geographically distributed applications with low and predictable latency. However, this technology also has significant research issues in its current stage such as successful implementation of service location models. In this article, we propose a deadline-aware and energy-efficient dynamic service placement (DEEDSP) technique for fog computing that supports the placement of IoT based services. Further, hyper-heuristic algorithm based energy-efficient service placement technique is proposed to balance the energy-delay trade-off based on different service placement decision criteria (eg, minimum response time or energy consumption).The proposed algorithm is able to dynamically minimize the energy consumption of the system while ensuring that the response time satisfies a given time constraint. Finally, the proposed technique is evaluated in simulated fog computing environment and experimental results show that this technique performs better than state-of-the-art placement techniques in terms of energy and latency.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Motivation and Our Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DEEDSP: Deadline‐aware and energy‐efficient dynamic service placement in integrated Internet of Things and fog computing environments

Raghavendra

Chawla

Gill

2021

Trans Emerging Tel Tech

Self Cite

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“…The emerging field of quantum computing extends a substantial computational lead over classical computing by leveraging the quantum physics principles of entanglement and superposition [ 147 ]. With unimaginably swift quantum computers, calculations are performed and stored using quantum bits referred to as qubits, which allows number crunching and problem-solving at an exponential scale.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Angel

Ravindran

Vincent

et al. 2021

Sensors

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Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects.

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On proposing an intelligent model for tracking agrochemicals

Monteiro

Mignoni

Righi

et al. 2022

Internet Technology Letters

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Agrochemicals are products that, due to their hazardous nature and high cost, need to be monitored. The management of agrochemical packaging is generally precarious, and the supply chain needs more control due to its reverse characteristic. Traceability in the supply chain usually uses one sensor and not a combination of multiple sensors. The proposed model allows one to trace agrochemicals with reliable and immutable information coming from various sensors, solving problems of unreliable traceability, product theft, and product tampering. Unlike related work, this model contributes with a proposal segmented in modules that focus on security and scalability in controlling used packaging. In this case, the proofs of concept indicate detection of the opening movement of the safe cabinet from 5 lx (unit of illuminance) and movement of packages after a radius of 2 cm, and the data sending time between the model layers was around 1 second. The positive aspects are benefits for detecting intact products and packages openly within the production process and persistently; other benefits include better use of assets and management of the production chain, real‐time production data collection, classification, grouping, and prediction of events. Monitoring the packaging reverse chain and the possibility of transaction auditing. Benefits include better use of assets and management of the production chain, real‐time production data collection, classification, grouping, and prediction of events generated by the production operation, and persistence of this information for future transactions and audits. Farmers and society benefit from making production and supply chains cleaner and safer and reducing the risk of costly and environmental accidents.

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Quantum and blockchain based Serverless edge computing: A vision, model, new trends and future directions

Cited by 57 publications

References 29 publications

DEEDSP: Deadline‐aware and energy‐efficient dynamic service placement in integrated Internet of Things and fog computing environments

DEEDSP: Deadline‐aware and energy‐efficient dynamic service placement in integrated Internet of Things and fog computing environments

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

On proposing an intelligent model for tracking agrochemicals

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