A lightweight smart contracts framework for blockchain‐based secure communication in smart grid applications

Faheem, Muhammad; Kuusniemi, Heidi; Eltahawy, Bahaa; Bhutta, Muhammad Shoaib; Raza, Basit

doi:10.1049/gtd2.13103

Cited by 31 publications

(11 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results show that the EMD-KPCA-BiLSTM-ATT combined model is superior to the other six models in predicting different data. (6) As can be seen from the prediction data of May 20 to 30 in Figure 7c and Table 4, MAE, MAPE, RMSE, and R2 are 9.865, 9.86%, 5.547, and 0.945, respectively. Even in the mediumand long-term wind power prediction, the proposed model still has good performance.…”

Section: Comparative Analysis Of the Prediction Resultsmentioning

confidence: 83%

“…With rapid progress in computer science and artificial intelligence [6], data-driven neural network models have made significant strides in wind power prediction. Models such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) have been used for wind power prediction [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model

Zhang,

Deng,

Wang

et al. 2024

Energies

View full text Add to dashboard Cite

In order to improve wind power utilization efficiency and reduce wind power prediction errors, a combined prediction model of EMD-KPCA-BilSTM-ATT is proposed, which includes a data processing method combining empirical mode decomposition (EMD) and kernel principal component analysis (KPCA), and a prediction model combining bidirectional long short-term memory (BiLSTM) and an attention mechanism (ATT). Firstly, the influencing factors of wind power are analyzed. The quartile method is used to identify and eliminate the original abnormal data of wind power, and the linear interpolation method is used to replace the abnormal data. Secondly, EMD is used to decompose the preprocessed wind power data into Intrinsic Mode Function (IMF) components and residual components, revealing the changes in data signals at different time scales. Subsequently, KPCA is employed to screen the key components as the input of the BiLSTM-ATT prediction model. Finally, a prediction is made taking an actual wind farm in Anhui Province as an example, and the results show that the EMD-KPCAM-BiLSTM-ATT combined model has higher prediction accuracy compared to the comparative model.

show abstract

Section: Comparative Analysis Of the Prediction Resultsmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model

Zhang,

Deng,

Wang

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…As an emerging technology, the integration of blockchain technology with the IoT for occupancy prediction in SBs has great potential for future development [ 159 ]. Blockchain-based IoT occupancy prediction has the potential to improve security and privacy, integrate with smart contracts, improve data quality and accuracy, integrate with artificial intelligence and ML, and be scalable and interoperable with other SB technologies in the future [ 160 , 161 ]. While it is important to recognize the significant costs associated with blockchain technology, it is equally important to underscore its potential in creating secure and energy-efficient buildings [ 162 ].…”

Section: Discussion Challenges and Future Directionmentioning

confidence: 99%

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Khan,

Zedadra,

Guerrieri

et al. 2024

Sensors

View full text Add to dashboard Cite

In today’s world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become “smart” and “cognitive” and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants’ data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy.

show abstract

“…In addition, it is assumed that all packets use the same physical rate and adopt the standard prescribed time slot duration and timing parameters. The fixed general parameter settings [14,15,25,26] of the various overhead introduced by the MAC layer due to priority recognition, leading codes, acknowledgements, and interframe space are shown in Tables 2 and 3.…”

Section: Simulation Parameter Settingsmentioning

confidence: 99%

Research on Hybrid Relay Protocol Design and Cross-Layer Performance Based on NOMA

Chen,

Cao,

Wang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Wireless and power line communication hybrid relay technology can realize complementary advantages and comprehensively improve the communication coverage and performance of power Internet of Things. In order to study the mechanism of the physical layer and Media Access Control (MAC) layer algorithm that affects the performance of hybrid relay systems, the cross-layer performance modeling, optimization, and simulation analysis are carried out for the non-orthogonal multiple access (NOMA) technology. Firstly, a two-hop NOMA media access control protocol is designed based on the CSMA algorithm. Considering the effects of non-ideal channel transmission at the physical layer and competitive access at the MAC layer on the system performance, a cross-layer performance analysis model of hybrid wireless and power line communication relay system under NOMA is established. Finally, a cross-layer optimization model based on multi-objective programming is established for the hybrid relay system. By analyzing the relationship between transmitting power and performance index, the joint optimization of transmitting power and power distribution factor between users is realized. Simulation results verify the validity and reliability of the proposed cross-layer model. The results show that the hybrid relay algorithm combined with NOMA and CSMA can effectively improve the performance of the system throughput, packet loss probability, and delay.

show abstract

A lightweight smart contracts framework for blockchain‐based secure communication in smart grid applications

Cited by 31 publications

References 39 publications

Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model

Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Research on Hybrid Relay Protocol Design and Cross-Layer Performance Based on NOMA

Contact Info

Product

Resources

About