Industrial IoT Data Scheduling Based on Hierarchical Fog Computing: A Key for Enabling Smart Factory

Chekired, Djabir Abdeldjalil; Khoukhi, Lyes; Mouftah, Hussein T.

doi:10.1109/tii.2018.2843802

Cited by 179 publications

(54 citation statements)

References 13 publications

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“…In Chekired et al, 16 a stochastic queuing model with Markov chain for transmitting between the states with priority queuing transmits the data. It serves industrial request.…”

Section: Schedulingmentioning

confidence: 99%

Data dissemination with interoperability in IoT network

Rathanasalam¹,

Kanagasabai²

2020

Int J Communication

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Summary Internet of Things (IoT) is connected to heterogeneous devices. Efficient adaptive scheduling with encoding and decoding of data is an unaddressed issue in IoT. This paper processes the data under three major hierarchy: namely, adaptability, scheduling of data, and network coding for that data. The reliable access to the information is ensured by a device which is a primary eminence in IoT. Device must be able to adapt itself according to the changes in the network and to maintain its reliability as well as transparency and seamless access to the resources. To enhance the performance of the data dissemination, the scheduling process is investigated using the spatial grouping in IoT devices; this is achieved by joint spatial and code domain scheduling scheme, and the novel preconfigured access scheme is coined in order to minimize the collision rate of arbitrary access; during the data dissemination, the erasure coding scheme is used for the encoding and decoding of packets which provides optimal redundancy. We carried the simulation using Contiki and it shows the proposed Polymorphic Erasure Coding with Markov decision Adaptability and Neural networks (PECMAN) improves in terms of cost, overhead, and delay when compared with Multi‐user Shared Access (EMUSA), Polynomial‐time Optimal Storage Allocation (OSA) scheme, and Event‐Aware Back pressure Scheduling Scheme (EABS).

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“…In Chekired et al, 16 a stochastic queuing model with Markov chain for transmitting between the states with priority queuing transmits the data. It serves industrial request.…”

Section: Schedulingmentioning

confidence: 99%

Data dissemination with interoperability in IoT network

Rathanasalam¹,

Kanagasabai²

2020

Int J Communication

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“…Fu et al [9] illustrated secure data storage and searching method to improve the efficiency and security of data storage and retrieval. Chekired et al [10] proposed a two-priority queuing model to schedule and analyze industrial data. Suganuma et al [11] proposed a flexible and advanced system model.…”

Section: Related Workmentioning

confidence: 99%

Edge Computing in an IoT Base Station System: Reprogramming and Real‐Time Tasks

Sun

et al. 2019

Complexity

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There are millions of base stations distributed across China, each containing many support devices and monitoring sensors. Conventional base station management systems tend to be hosted in the cloud, but cloud-based systems are difficult to reprogram and performing tasks in real-time is sometimes problematic, for example, sounding a combination of alarms or executing linked tasks. To overcome these drawbacks, we propose a hybrid edge-cloud IoT base station system, called BSIS. This paper includes a theoretical mathematical model that demonstrates the dynamic characteristics of BSIS along with a formulation for implementing BSIS in practice. Embedded programmable logic controllers serve as the edge nodes; a dynamic programming method creates a seamless integration between the edge nodes and the cloud. The paper concludes with a series of comprehensive analyses on scalability, responsiveness, and reliability. These analyses indicate a possible 60% reduction in the number of alarms, an edge response time of less than 0.1s, and an average downtime ratio of 0.66%.

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“…On the other hand, a generic hierarchical IoT architecture shown in Figure 1 is taken into account in this paper [17]. Such hierarchical architecture plays an irreplaceable role in cloud computing [18], access control [19] and data scheduling [20] for IoT network. In such a system, controller and IoT region servers usually have their own processing resource objectives [21].…”

Section: Introductionmentioning

confidence: 99%

Large Scale Resource Allocation for the Internet of Things Network Based on ADMM

Zhang

Tang

et al. 2020

IEEE Access

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Large scale deployment of Internet of Things (IoT) devices poses challenges in resource allocation. In this paper, alternating direction method of multipliers (ADMM) is adopted to solve such large scale resource allocation problems. Based on this, three optimization problems are investigated in a hierarchical IoT network. Considering ADMM could not solve a non-convex optimization problem directly, a non-convex fractional programming problem i.e., energy efficiency maximization problem for IoT region server, is formulated. Faced with this problem, we introduce the Dinkelbach algorithm to transfer the energy efficiency maximization problem into an equivalent convex optimization problem. Then the classic ADMM with two blocks is employed to solve the equivalent convex optimization problem. On the other hand, the classic ADMM with two blocks could not satisfy the convergence speed demands of the high-dimensional convex optimization problems any more. Thus, the network latency minimization problem for controller is designed and then solved by the Jacobian-ADMM algorithm in parallel. It is hard to satisfy controller and IoT region servers' objectives at the same time. Given this, an incentive mechanism on the basis of Stackelberg game is designed. Thus a game-based resource allocation problem is proposed to deal with the contradiction between the centralized objective of the controller and the individual objectives from the IoT region servers. Based on the Dinkelbach algorithm and Jacobian-ADMM algorithm, a two-layer iterative resource allocation algorithm is posed to solve the game-based resource allocation problem. Last but not least, the convergence of the proposed algorithms are analyzed with numerous simulation results.

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Industrial IoT Data Scheduling Based on Hierarchical Fog Computing: A Key for Enabling Smart Factory

Cited by 179 publications

References 13 publications

Data dissemination with interoperability in IoT network

Data dissemination with interoperability in IoT network

Edge Computing in an IoT Base Station System: Reprogramming and Real‐Time Tasks

Large Scale Resource Allocation for the Internet of Things Network Based on ADMM

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