With the development of the smart grid, massive electric Internet of Things (EIoT) devices are deployed to collect data and offload them to edge servers for processing. However, the task of offloading optimization still faces several challenges, such as the differentiated quality of service (QoS) requirements, decision coupling among multiple devices, and the impact of electromagnetic interference. In this paper, a low-complexity delay and energy-efficiency-balanced task offloading algorithm based on many-to-one two-sided matching is proposed for an EIoT. The proposed algorithm shows its novelty in the dynamic tradeoff between energy efficiency and delay as well as in low-complexity and stable task offloading. Specifically, we firstly formulate the minimization problem of weighted difference between delay and energy efficiency to realize the joint optimization of differentiated QoS requirements. Then, the joint optimization problem is transformed into a many-to-one two-sided matching problem. Through continuous iteration, a stable matching between devices and servers is established to cope with decision coupling among multiple devices. Finally, the effectiveness of the proposed algorithm is validated through simulations. Compared with two advanced algorithms, the weighted difference between the energy efficiency and delay of the proposed algorithm is increased by 29.01% and 45.65% when the number of devices is 120, and is increased by 11.57% and 22.25% when the number of gateways is 16.
A method of optical fiber composite overhead ground wire (OPGW) positioning based on a Brillouin distributed optical fiber sensor and machine learning is proposed. A distributed Brillouin optical time-domain reflectometry (BOTDR) and Brillouin optical time-domain analyzer (BOTDA) are designed, where the ranges of BOTDR and the BOTDA are 110 km and 125 km, respectively. An unsupervised machine learning method density-based spatial clustering of applications with noise (DBSCAN) is proposed to automatically identify the splicing point based on the Brillouin frequency shift (BFS) difference of adjacent sections. An adaptive parameter selection method based on k-distance is adapted to overcome the parameter sensitivity. The validity of the proposed DBSCAN algorithm is greater than 96%, which is evaluated by three commonly external validation indices with five typical BFS curves. According to the clustering results of different fiber cores and the tower schedule of the OPGW, the connecting towers are distinguished, which is proved as a 100% recognition rate. According to the identification results of different fiber cores of both the OPGW cables and tower schedule, the connecting towers can be distinguished, and the distributed strain information is extracted directly from the BFS to strain. The abnormal region is positioned and warned according to the distributed strain measurements. The method proposed herein significantly improves the efficiency of fault positioning and early warning, which means a higher operational reliability of the OPGW cables.
For conservation and environmental purposes, three electricity consumption models, power grid, power grid + PV and power grid + PV + battery, are proposed in this paper, the optimization of the electric heating energy consumption model is carried out with the objective of the highest annual net return of the system. The results showed that for the single-layer residence with a housing area of 100m2, the use of the power grid + photovoltaic system has the highest annual net income, as well as good economic and environmental benefits.
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