Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

Trans Emerging Tel Tech

2020

The rapid growth of the number of sensing devices enables computation offloading to be a promising solution to alleviate the burden of core network communication and provide low delay services, especially for those computation-intensive and delay-sensitive tasks. For meeting the processing requirements of these tasks sufficiently, a latency-minimum offloading decision and resource allocation scheme for fog-enabled Internet of Things (IoT) networks is developed in this article. Specifically, we formulate a joint optimization problem of the offloading decision, the local computation capability, and the computing resource allocation of fog node to minimize the task completion time with energy constraint, in which practically considers M/M/1 waiting queues in the wireless channel and fog node. To solve this mixed integer nonlinear programming problem with low complexity, we first calculate the optimal values of local computation capability and computing resource allocation of fog node by decomposing the original optimization problem into two independent subproblems. Subsequently, we propose a hybrid genetic simulated annealing-based latency-minimum offloading decision algorithm to optimize the offloading decision. Finally, the numerical results verify that our proposed scheme achieves significant advantages compared to other alternative schemes in terms of completion time and energy consumption, and they also confirm the advantage of convergence speed and quality.

Section: Discussionmentioning

confidence: 99%

Latency‐minimum offloading decision and resource allocation for fog‐enabled Internet of Things networks

Wang

Trans Emerging Tel Tech

2020

“…Finally, experiment results further prove the advantages of our scheme in terms of computation and communication costs. In our future work, we will integrate the data spatiotemporal compression, flexible aggregation, network resource optimization theory, and machine learning methods into our scheme for constructing a more efficient and smart data aggregation scheme.…”

Section: Discussionmentioning

confidence: 99%

Layered data aggregation with efficient privacy preservation for fog‐assisted IIoT

Zhao

et al. 2020

Int J Communication

The emergence of fog computing facilitates industrial Internet of Things (IIoT) to be more real-time and efficient; in order to achieve secure and efficient data collection and applications in fog-assisted IIoT, it usually sacrifices great computation and bandwidth resources. From the low computation and communication overheads perspective, this paper proposes a layered data aggregation scheme with efficient privacy preservation (LDA-EPP) for fog-assisted IIoT by integrating the Chinese remainder theorem (CRT), modified Paillier encryption, and hash chain technology. In LDA-EPP scheme, the entire network is divided into several subareas; the fog node and cloud are responsible for local and global aggregations, respectively. Specially, the cloud is able to obtain not only the global aggregation result but also the fine-grained aggregation results of subareas, which enables that can provide fine-grained data services. Meanwhile, the LDA-EPP realizes data confidentiality by the modified Paillier encryption, ensures that both outside attackers and internal semi-trusted nodes (such as, fog node and cloud) are unable to know the privacy data of individual device, and guarantees data integrity by utilizing simply hash chain to resist tempering and polluting attacks. Moreover, the fault tolerance is also supported in our scheme; ie, even though some IIoT devices or channel links are failure, the cloud still can decrypt incomplete aggregation ciphertexts and derive expected aggregation results. Finally, the performance evaluation indicates that our proposed LDA-EPP has less computation and communication costs.

“…To achieve a high performance and efficient mass production of nanoparticles, flow processes and reactors have emerged as the main focus in which continuous operation provides better heat and mass transfer, reducing the cost and waste throughout the process as well as delivering higher yields. [ 110–112 ] To date, several continuous synthesis routes to obtain UCNPs have been classified and reported as follows.…”

Section: Continuous Reactorsmentioning

confidence: 99%

Controllable Synthesis of Upconversion Nanophosphors toward Scale‐Up Productions

Jiao

Part & Part Syst Charact

Wang

et al. 2020

Upconversion nanophosphors (UCNPs) are considered as an important synthesis arm within biomedical and energy sectors due to their unique optical characteristics, which can convert near‐infrared light into higher energy emissions. However, key challenges, cost, compatibility of the materials, etc. have to be taken into serious consideration to transform this in‐lab UCNPs technology into scale‐up production for wider commercial needs. This review highlights the fundamental concepts of synthetic approaches for UCNPs and recaps recent advances in terms of large‐scale production. A number of typical synthesis routes in both batch and continuous processes are reviewed, alongside their limitations and potential improvements when being considered for mass production. By discussing and exploiting the technical compacity for the potential synthetic trends, key challenges, and expectations of future synthesis methods for UCNPs are also outlined.