IoT-Fog Optimal Workload via Fog Offloading

Al-Khafajiy, Mohammed; Baker, Thar; Waraich, Atif; Al‐Jumeily, Dhiya; Hussain, Abir

doi:10.1109/ucc-companion.2018.00081

Cited by 60 publications

(33 citation statements)

References 16 publications

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“…The reputation score and the credibility score of endpoint devices are continuously updated, considering the feedback following each round of fog-device interactions. However, due to the abundance of fog service providers, the mentioned solution lacks a systematic way for endpoint devices to choose the best fog service provider for specific tasks considering the fairness and efficiency requirements of endpoint mobile and IoT applications [33], [34]. Hence, a smart contract-based auctioning mechanism would be a suitable solution to manage the communication between fog nodes and endpoint devices until they reach an agreement on the expected QoS and prices of required services.…”

Section: Related Workmentioning

confidence: 99%

Blockchain-Based Decentralized Reverse Bidding in Fog Computing

2020

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Fog computing systems are designed to provide localized computation, storage, and communication services in close proximity to the endpoint mobile and IoT devices. Fog service providers typically monetize their service usage via centralized payment mechanisms in unverifiable and non-transparent manner. Therefore, there exists a need for a trust-enabling payment mechanism whereby fog service providers should be incentivized or penalized based upon the continuous feedback from endpoint devices. We propose a decentralized reverse-bidding scheme developed using the key features of blockchain and smart contracts. We develop a solution that allows the users or devices to initiate the bidding process by making a request for services to be provided by nearby public fog nodes, and these fog nodes to make bid offers in return. The proposed scheme ensures that all fog nodes on the network can equally and fairly make offers to win the bid. The bidding process incorporates the automated payments at the end of the service. Our solution is implemented using Ethereum smart contracts. It also integrates a reputation system for fog nodes and imposes a penalty for misbehaving nodes. Our solution is fully decentralized and provides a high level of trust, transparency, and security. In the paper, we present the system architecture, implementation details, and show the correct functionality of the overall proposed solution. In addition, we provide performance, cost, and security analyses of the smart contract code to demonstrate its effectiveness and robustness against major security concerns. The results show that the cost of running the smart contract remained less than three cents with the current Ethereum price (i.e., 183.22 USD/Eth). We have also made our smart contract code publicly available on Github. 1

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Section: Related Workmentioning

confidence: 99%

Blockchain-Based Decentralized Reverse Bidding in Fog Computing

2020

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“…Besides, the authors in [15] propose a resource scheduling scheme for minimizing the energy consumption incurred when offloading computational work from mobile applications to cloud servers. Recently, a network paradigm, called fog computing, is exploited to provide work offloading services for IoT devices [16], [17]. The 'Fog' indicates a highly virtualized platform that provides the application and network services for various devices using the cloud infrastructure.…”

Section: Related Workmentioning

confidence: 99%

Queuing Model Based Edge Placement for Work Offloading in Mobile Cloud Networks

2020

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Recently, many useful services have been provided through wireless mobile devices. One of the critical issues is that mobile devices are generally built with limited computing capability, and thus may not be able to handle the complexity of many advanced services. This problem can be mitigated by offloading certain jobs from the mobile devices to the edge or cloud servers. However, the locations of the edge servers and the allocation of the offloading jobs to the edge servers can significantly affect the traffic generated in the network. Accordingly, this study conducts an investigation into the edge server placement and work allocation strategy with a focus on minimizing the total traffic load so as to achieve green communications in the mobile cloud networks. Mobile devices are assumed to be attached to the nearby node and they may send work offloading requests to the attached nodes. Meanwhile, edge servers are deployed at carefully selected nodes. The offloading jobs received by a node are assigned to a selected edge server. If the edge server is busy, the work will be forwarded to the central cloud server. Otherwise, it is processed locally in the edge server. A queuing model is adopted to evaluate the job forwarding probability at the edge servers. The problem is formulated as an integer programming problem. Two novel heuristic schemes are proposed, namely the Set-by-Set algorithm and the K-clustering algorithm. The simulation results showed that the K-clustering algorithm consistently outperforms both the Set-by-Set algorithm and the Density-Based-Clustering (DBC) algorithm presented in the literature in terms of a lower total traffic load within the network. INDEX TERMS Edge computing, cloud networks, work offloading, green communications, edge server placement.

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“…The fog layer provides more performance and security of the high Quality of Service (QoS) required of a large-scale application [23]. One of the expected benefits of using the fog cloud is to decrease the response time between the subscribers and publishers.…”

Section: The Mqtt/uma Hybrid Modelmentioning

confidence: 99%

A Hybrid IoT Security Model of MQTT and UMA

Aloufi¹,

Alhazmi²

2020

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IoT-Fog Optimal Workload via Fog Offloading

Cited by 60 publications

References 16 publications

Blockchain-Based Decentralized Reverse Bidding in Fog Computing

Blockchain-Based Decentralized Reverse Bidding in Fog Computing

Queuing Model Based Edge Placement for Work Offloading in Mobile Cloud Networks

A Hybrid IoT Security Model of MQTT and UMA

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