Fog computing systems: State of the art, research issues and future trends, with a focus on resilience

Moura, José; Hutchison, David

doi:10.1016/j.jnca.2020.102784

Cited by 41 publications

(16 citation statements)

References 172 publications

(249 reference statements)

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“…While we recognise that there is a wealth of research in ML based resilience and dependability, e.g. summarised in review papers such as Moura & Hutchinson (2019), for this paper we limited the scope to the development of the conceptual aspects of the resilience assurance model and DRAs. The system of learning for resilience assurance provides an important model for the integration of RAS lifecycle view based on a continuous product development paradigm, with the broader socio-technic systems perspective of users and stakeholders, where the trust in resilience of the system is ultimately evidenced.…”

Section: Discussion Conclusion and Further Workmentioning

confidence: 99%

“…However, the broader focus on resilience capability of the system requires the DRAs to integrate both the current state of the system in light of the time-dependent degradation in capacity of the system (both physical and software) to react to mission and environment events, as well the characteristic of the threat or attack. Current research in CPS resilience tends to focus on the latter (see for example Moura & Hutchinson (2019) and Bagchi et al (2020)), with a primary concern for the software systems. As RAS become increasingly common for more diverse applications, the users' and stakeholders' trust in the resilience of the systems will depend on their experience of the system, and the way the system is able to fulfil the expected "fit for purpose" criteria.…”

Section: Discussion Conclusion and Further Workmentioning

confidence: 99%

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Towards a Resilience Assurance Model for Robotic Autonomous Systems

et al. 2021

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Applications of autonomous systems are becoming increasingly common across the field of engineered systems from cars, drones, manufacturing systems and medical devices, addressing prevailing societal changes, and, increasingly, consumer demand. Autonomous systems are expected to self-manage and self-certify against risks affecting the mission, safety and asset integrity. While significant progress has been achieved in relation to the modelling of safety and safety assurance of autonomous systems, no similar approach is available for resilience that integrates coherently across the cyber and physical parts. This paper presents a comprehensive discussion of resilience in the context of robotic autonomous systems, covering both resilience by design and resilience by reaction, and proposes a conceptual model of a system of learning for resilience assurance in a continuous product development framework. The resilience assurance model is proposed as a composable digital artefact, underpinned by a rigorous model-based resilience analysis at the system design stage, and dynamically monitored and continuously updated at run time in the system operation stage, with machine learning based knowledge extraction and validation.

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Section: Discussion Conclusion and Further Workmentioning

confidence: 99%

Section: Discussion Conclusion and Further Workmentioning

confidence: 99%

Towards a Resilience Assurance Model for Robotic Autonomous Systems

et al. 2021

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“…As previously stated, one way to view Fog Networks is to envision it as a Cyber-physical system. There are numerous applications of this visualization [36].They include smart buildings, power grids and connected vehicles. The design concerns for individual realization are largely developed through machine learning models.…”

Section: Future Researchmentioning

confidence: 99%

“…The design concerns for individual realization are largely developed through machine learning models. Yet the optimal choice of a specific machine learning algorithm based on a particular scenario is still unanswered.The design aspects such as energy efficiency, computational offloading and reduced latency are increasingly important research areas [36].…”

Section: Future Researchmentioning

confidence: 99%

A Comparative Study of Various Machine Learning Algorithms in Fog Computing

2021

IJATCSE

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Internet of Things is the reality of the future. It encompasses a vast prototype. The applications are limitless and the research potential is huge. Several unanswered questions still stand in the way. One of these challenging questions is the Network layer implementation of IoT. Cloud offers one perspective solution. Cloud services face the down side of connectivity and latency. Fog computing, as a complementing technology for Cloud answers the issues of latency and connectivity in the Cloud. Being novel idea in itself, Fog computing offers promising future growth. A major concept in Fog networks is its ability to learn and adopt to changing environment. This ability of the Fog devices to self-learn and improve itself is the core of machine-learning. Hence, machine-learning algorithms occupy central place in visualizing robust and effective Fog networks. This paper presents a comparative study of various machine-learning algorithms and their applications in the Fog network. The paper also projects the undiscovered research paradigms and future directions

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“…Hence, IoTs need to share the process schedulers (i.e., packet scheduling) of the fog end for data allocation procedures and increase the delay, producing data stress among the distributed fog data slots. The use of a valid developed scheme through proper scheduling or resource allocation may tune the fog services well, allowing integrated fog-IoT orchestrations [10].…”

Section: Introductionmentioning

confidence: 99%

BFIM: Performance Measurement of a Blockchain Based Hierarchical Tree Layered Fog-IoT Microservice Architecture

et al. 2021

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Fog computing complements cloud computing by removing several limitations, such as delays and network bandwidth. It emerged to support Internet of Things (IoT) applications wherein its computations and tasks are carried out at the network's edge. Heterogeneous IoT devices interact with different users throughout a network. However, data security is a crucial concern for IoT, fog and cloud network ecosystems. Since the number of anonymous users increases and new identity disclosures occur within the IoTs, it is becoming challenging to grow mesh networks to deliver end to end communications, as the extended IoT networks resemble a mesh architecture. To reinforce data security over IoTs, we deploy a microservice-based blockchain mechanism for fogs, which works as a decentralized client-server network medium (i.e., secured end device-based communication). We implement a blockchain equipped security scheme to be used with a fog-IoT hierarchical tree-based overlay mesh architecture to address and develop the network performance issues. In this study, we consider encryption and decryption delays from IoT and fog-integrated parts to monitor data records and compare them through the developed security scheme. The blocks of a blockchain offer the desired execution results mainly in terms of the algorithmic efficiency, which correlates with the existing algorithms, namely the Advanced Encryption Standard (AES), the Rivest Shamir Adleman (RSA), and the Data Encryption Standard (DES). Our 'BFIM' scheme has an enhanced task scheduling capacity and a more efficient throughput than the AES, DES, RSA resource deliverables (i.e., tasks). Our comprehensive performance evaluation implies that the Blockchain-based Fog IoT Microservice (i.e., BFIM) architecture provides a task delivery efficiency of 78.79% (i.e., task deliverable) and a service delivery efficiency of 83.24% (i.e., task scheduling). The 'BFIM' also has an overall process delivery efficiency of 75% (i.e., time delay, throughput) in the fog layer, rather than a central cloud layer running the AES, DES, and RSA algorithms.

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Fog computing systems: State of the art, research issues and future trends, with a focus on resilience

Cited by 41 publications

References 172 publications

Towards a Resilience Assurance Model for Robotic Autonomous Systems

Towards a Resilience Assurance Model for Robotic Autonomous Systems

A Comparative Study of Various Machine Learning Algorithms in Fog Computing

BFIM: Performance Measurement of a Blockchain Based Hierarchical Tree Layered Fog-IoT Microservice Architecture

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