DMTC: Optimize Energy Consumption in Dynamic Wireless Sensor Network Based on Fog Computing and Fuzzy Multiple Attribute Decision‐Making

Varmaghani, Abbas; Nazar, Ali Matin; Ahmadi, Mohsen; Sharifi, Abbas; Ghoushchi, Saeid Jafarzadeh; Pourasad, Yaghoub

doi:10.1155/2021/9953416

Cited by 55 publications

(16 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimal allocation of resources in the network help in minimizing the requirements for the system, power losses and cost. In real world problems, the optimal allocation of sensors in wireless sensor networks (WSNs) or Flexible AC Transmission Systems (FACTS) controllers in electrical transmission networks [ 25 , 26 ] have a profound impact on requirements of the system and network performance [ 27 ]. Like allocation of sensors or FACTS controllers, the optimal allocation of DG has a great impact on network power losses.…”

Section: Introductionmentioning

confidence: 99%

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

et al. 2022

View full text Add to dashboard Cite

The installation of Distributed Generation (DG) units in the Radial Distribution Networks (RDNs) has significant potential to minimize active power losses in distribution networks. However, inaccurate size(s) and location(s) of DG units increase power losses and associated Annual Financial Losses (AFL). A comprehensive review of the literature reveals that existing analytical, metaheuristic and hybrid algorithms employed on DG allocation problems trap in local or global optima resulting in higher power losses. To address these limitations, this article develops a parallel hybrid Arithmetic Optimization Algorithm and Salp Swarm Algorithm (AOASSA) for the optimal sizing and placement of DGs in the RDNs. The proposed parallel hybrid AOASSA enables the mutual benefit of both algorithms, i.e., the exploration capability of the SSA and the exploitation capability of the AOA. The performance of the proposed algorithm has been analyzed against the hybrid Arithmetic Optimization Algorithm Particle Swarm Optimization (AOAPSO), Salp Swarm Algorithm Particle Swarm Optimization (SSAPSO), standard AOA, SSA, and Particle Swarm Optimization (PSO) algorithms. The results obtained reveals that the proposed algorithm produces quality solutions and minimum power losses in RDNs. The Power Loss Reduction (PLR) obtained with the proposed algorithm has also been validated against recent analytical, metaheuristic and hybrid optimization algorithms with the help of three cases based on the number of DG units allocated. Using the proposed algorithm, the PLR and associated AFL reduction of the 33-bus and 69-bus RDNs improved to 65.51% and 69.14%, respectively. This study will help the local distribution companies to minimize power losses and associated AFL in the long-term planning paradigm.

show abstract

Section: Introductionmentioning

confidence: 99%

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

et al. 2022

View full text Add to dashboard Cite

show abstract

“…With their new detection method, Zong et al [38]applied a multiscale grouping (MSG) structure to a 3D point cloud tunnel dataset and applied a 3D-BoNet instance segmentation model. An optimization of energy consumption in dynamic wireless sensor networks using fog computing and fuzzy multiattribute decision-making was proposed by Varmaghani et al [39]. According to Zong and Wan [40], a 3D scanner can be used to acquire 3D data.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Esmaeili

Goki

Masjidi

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The Internet of Things (IoT) is a complicated security feature in which datagrams are protected by integrity, confidentiality, and authentication services. The network is protected from external interruptions and intrusions. Because IoT devices run with a range of heterogeneous technologies and process data over time, standard solutions may not be practical. It is necessary to develop intelligent procedures that can be used for multiple levels of data flow in the system. This study examines metainnovations using deep learning-based IDS. Per the findings of the earlier tests, BiLSTMs are better for binary (regular/attacker) classification; however, sequential models (LSTM or BiLSTM) are better for detecting some brutal attacks in multiclass classifiers. According to experts, deep learning-based intrusion detection systems can now recognize and select the best structure for each category. However, specific difficulties will need to be solved in the future. Two topics should be studied further in future attempts. One of the researchers’ concerns is the impact of various data processing techniques, such as artificial intelligence or metamethods, on IDS. The BiLSTM approach has chosen the safest instances with the highest accuracy among the models. According to the findings, the most reliable and suitable solution for evaluating DDoS attacks in IoT is the BiLSTM design.

show abstract

“…Author of Reference 31 exercise on forming cluster heads with balanced energy by selecting two cluster head depending upon the energies and distance between head and base station. Author of Reference 32 experienced that optimistic distribution of computing load shows better results as compared to the blind distribution. In this model clustering and routing was done using Fuzzy Multiple Attribute Decision Making technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Power‐efficient optimized clustering method with intelligent fog computing for wireless sensor networks

Jain

Bhardwaj

2022

Concurrency and Computation

View full text Add to dashboard Cite

One of the most essential characteristics that is taken into consideration while dealing with wireless sensor networks (WSNs) is to optimize energy consumption during the transmission of data packets. Routing algorithms must provide optimal solutions to reduce the amount of energy consumed during the process. In wireless sensor networks, the end devices layer comprises of clustering of sensor nodes using ant lion optimization (ALO) which is followed by fog layer consisting of k-means clustering technique. When nature inspired optimization solution is implemented, the accuracy majorly depends upon complexity and number of parameters. The algorithm proposed majorly focuses on the bottom layer and not on the fog layer. The results of the proposed algorithm were proved to significantly better than the conventional algorithms used for the very same purpose. The proposed algorithm focuses on ALO only on the bottom layer and not on the fog layer. To evaluate the performance of the proposed algorithm, the results are compared with the findings of several traditional algorithms, such as energy-efficient cross-layer-sensing clustering method, Distributed and Morphological Operation-based Data Collection Algorithm, and trust-based secure routing. Comparative results showed that the proposed algorithm presented significant improvement.

show abstract

DMTC: Optimize Energy Consumption in Dynamic Wireless Sensor Network Based on Fog Computing and Fuzzy Multiple Attribute Decision‐Making

Cited by 55 publications

References 49 publications

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

Parallel operated hybrid Arithmetic-Salp swarm optimizer for optimal allocation of multiple distributed generation units in distribution networks

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Power‐efficient optimized clustering method with intelligent fog computing for wireless sensor networks

Contact Info

Product

Resources

About