Corrigendum: Integration of Internet of Things and cloud computing: a systematic survey

Jahantigh, Motahareh Nazari; Rahmani, Amir Masoud; Navimipour, Nima Jafari; Rezaee, Ali

doi:10.1049/iet-com.2021.0001

Cited by 7 publications

(6 citation statements)

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“…The comprehensive testbed and system modelling evaluation presented in this paper would enable industrial system's practitioners to adopt our model, especially if their focus is to enhance computing efficiency, reduce memory and target faster node discoveries within remote systems. Further steps at improving the bandwidth‐constrained scheme with deep learning using multi‐core cooperative scheduling techniques [34, 35, 36, 37, 38] are major future research concerns and directions.…”

Section: Discussionmentioning

confidence: 99%

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MAD‐DDS: Memory‐efficient automatic discovery data distribution service for large‐scale distributed control network

et al. 2023

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The rampant deployment of data distribution service (DDS) as middle‐ware service providers for industrial network platforms has been widely investigated. All DDS‐based node discovery protocols establish communication with its intended target systems by accessing matched endpoints/nodes information. These matched endpoints/nodes information is usually embedded with the system’s programmable control plane network which acts as the conveyor vehicle. The introduction of software defined networking (SDN) is to characterize the control plane from embedded data plane. The DDS implements the simple discovery protocol (SDP) as its inherent node discovery protocol. Deploying DDS for data packet exchange in server‐based collaborative distributed networked control (DNC) systems has gained traction. The current automatic discovery protocol (ADP) based on SDP is fraught with real‐time limitations such as high memory consumption and poor packet transmission. This work presents novel memory‐efficient automatic discovery data distribution service (MAD‐DDS) with enhanced threshold bloom filters (ETBF) where ETBF stores transmission packets at simulation end‐nodes. The packet is further adjusted using optimized binarization and decision thresholds inside ADP, hence guaranteeing memory reduction. The testbed computation recorded significantly improved quality of service (QoS) whereas numerical results depict significant decline in memory consumption with consistent packet transmission rates that produces increased computational capacity.

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Section: Discussionmentioning

confidence: 99%

“…discoveries within remote systems. Further steps at improving the bandwidth-constrained scheme with deep learning using multi-core cooperative scheduling techniques [34,35,36,37,38] are major future research concerns and directions.…”

Section: Author Contributionsmentioning

confidence: 99%

MAD‐DDS: Memory‐efficient automatic discovery data distribution service for large‐scale distributed control network

et al. 2023

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“…The cloud-fog collaborative computing network model based on the IoT heterogeneous scenario mainly solves the problem of data transmission and processing between a large number of heterogeneous devices in the IoT, and optimises the allocation of resource capacity to realise the various service functions of the IoT [6][7] . And by combining cloud computing and fog computing, an efficient, green, secure and reliable IoT network model is constructed, which can effectively solve this problem.…”

Section: Construction Of a Resource Optimization Model For Iot Hetero...mentioning

confidence: 99%

An IGA model for resource allocation in heterogeneous networks for the internet of things

Xiao

2023

Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023)

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To reasonably allocate the heterogeneous network resources of Internet of Things (IoT) to meet the various needs of users in a cost-effective manner. The study proposes an improved genetic algorithm (IGA) method for finding the optimal solution of heterogeneous network resource allocation. The method first transforms the IoT heterogeneous network resource allocation optimization problem into an objective function to find the optimal solution problem, then uses the improved genetic algorithm to find the optimal solution under the constraints, and finally analyses and simulates the performance of the model for application. The results show that the algorithm stabilises after 10 iterations in the IoT-23 dataset and 14 iterations in the IoT-Healthcare dataset, with stability values of approximately 3.16 and 3.14 respectively, and that the computation time of the algorithm is 8.8m and 3.6m in the two datasets respectively when the number of users is 100, which is much less than the other two algorithms. In the simulation experiments on the accuracy of the calculation results, the study achieved an accuracy rate of over 92% and 94% in A and B respectively. Combining the above data, it can be shown that the method can accurately achieve the efficient allocation and utilisation of resources under the constructed network environment and demand.(Abstract)

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“…Thus, systems based solely on a centralised cloud infrastructure have limitations that may hinder the implementation of certain Internet of Things (iot) applications. Indeed, computing a huge amount of data at the central cloud will have the disadvantage of generating significant latency combined with high storage and bandwidth costs that are detrimental to the proper functioning of delay‐sensitive IoT applications [1].…”

Section: Introductionmentioning

confidence: 99%

A new clustering‐based optimised energy approach for fog‐enabled IoT networks

2023

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The Internet of Things (IoT) has induced many advances in the modern world, thus always arousing great interest from the scientific community. Due to a large number of connected devices, this paradigm has put into circulation an enormous data quantity to be processed and offloaded while respecting latency and energy constraints that Central Cloud alone cannot meet. Hence, Fog Computing has come to fill these gaps by providing computing, management, and storage resources via small‐distributed data centres located at the network edge. The majority of previous research on centralised clustering in an IoT environment did not consider both residual energy and the location of the IoT devices, as well as the signalling communication frequency, to solve an issue of extreme energy consumption during data exchange between IoT devices and Fog entities. Thus, the objective of this study is to find solutions taking into account these criteria to guarantee the Fog system's energy efficiency while coping with the energy consumption increase related to the handling of massive data volume. Indeed, the proposed study presents a novel approach based on Fog‐IoT architecture to ensure intelligent energy management during communication and offloaded task processing in IoT applications. The simulation results show its effectiveness.

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Corrigendum: Integration of Internet of Things and cloud computing: a systematic survey

Cited by 7 publications

References 0 publications

MAD‐DDS: Memory‐efficient automatic discovery data distribution service for large‐scale distributed control network

MAD‐DDS: Memory‐efficient automatic discovery data distribution service for large‐scale distributed control network

An IGA model for resource allocation in heterogeneous networks for the internet of things

A new clustering‐based optimised energy approach for fog‐enabled IoT networks

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