Blocking-Resilient Communications in Information-Centric Networks Using Router Redirection

Mozaffari, Hamid; Houmansadr, Amir; Venkataramani, Arun

doi:10.1109/gcwkshps45667.2019.9024405

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…In contrast, many reasons like randomness, ambiguity, and vagueness make uncertainty in image processing results and data. Furthermore, those uncertainties have a negative impact on image processing progress that leads to many complications [39][40][41].…”

Section: Fuzzy Logicmentioning

confidence: 99%

Offering a Demand‐Based Charging Method Using the GBO Algorithm and Fuzzy Logic in the WRSN for Wireless Power Transfer by UAV

et al. 2023

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An extremely high number of geographically dispersed, energy-limited sensor nodes make up wireless sensor networks. One of the critical difficulties with these networks is their network lifetime. Wirelessly charging the sensors continuously is one technique to lengthen the network’s lifespan. In order to compensate for the sensor nodes’ energy through a wireless medium, a mobile charger (MC) is employed in wireless sensor networks (WRSN). Designing a charging scheme that best extends the network’s lifetime in such a situation is difficult. In this paper, a demand-based charging method using unmanned aerial vehicles (UAVs) is provided for wireless rechargeable sensor networks. In this regard, first, sensors are grouped according to their geographic position using the K-means clustering technique. Then, with the aid of a fuzzy logic system, these clusters are ranked in order of priority based on the parameters of the average percentage of battery life left in the sensor nodes’ batteries, the number of sensors, and critical sensors that must be charged, and the distance between each cluster’s center and the MC charging station. It then displays the positions of the UAV to choose the crucial sensor nodes using a routing algorithm based on the shortest and most vital path in each cluster. Notably, the gradient-based optimization (GBO) algorithm has been applied in this work for intracluster routing. A case study for a wireless rechargeable sensor network has been carried out in MATLAB to assess the performance of the suggested design. The outcomes of the simulation show that the suggested technique was successful in extending the network’s lifetime. Based on the simulation results, compared to the genetic algorithm, the proposed algorithm has been able to reduce total energy consumption, total distance during the tour, and total travel delay by 26%, 17.2%, and 25.4%, respectively.

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Section: Fuzzy Logicmentioning

confidence: 99%

Offering a Demand‐Based Charging Method Using the GBO Algorithm and Fuzzy Logic in the WRSN for Wireless Power Transfer by UAV

et al. 2023

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“…., x k 􏼈 􏼉 is called a chromosome and x i is a gene. Ten, three standard genetic operations, namely, reproduction, crossover, and mutation, are accomplished to produce the new generation [42,43]. Such procedures are repeated until the number of predetermined generations can be achieved or accurately required.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

A New Method for Fault Detection and Location in a Low-Resistance Grounded Power Distribution Network Using Voltage Phasor of D-PMUs Data

Najafzadeh

Pouladi

Daghigh

et al. 2023

International Transactions on Electrical Energy Systems

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Electric power grids are always affected by numerous unexpected faults. Occurrence of these faults will have a negative impact on network availability and reliability indices of the network. But the indicators of reliability and quality of electrical energy in the network can be augmented by locating the fault in the shortest time. Special features of distribution networks such as load and network imbalance, existence of different types of load with different connections, existence of multiphase branches, effects related to different conductors, capacitive effects of distribution lines, and limited numbers of measuring devices complicate the process of fault localization in distribution networks. On the other hand, increasing penetration of distributed generation units has caused conventional methods of fault localization. Therefore, it is mandatory to introduce new methods of fault locating by considering the mentioned features. Hence, in the current study, nonlinear methods are presented for identifying the location of ground faults in the power distribution network with the help of voltage phasor measurement at different network buses by the D-PMU phased distribution unit. In the first technique, the genetic optimization algorithms and particle swarm optimization for nonlinear modeling of fault position along the distribution line have been utilized for different single-phase, two-phase, and three-phase faults, and in the second technique, neural fuzzy network training has been proposed by different phasor measurement devices. In this case, it is enough to access the phase information of the network bus voltage. In order to show the effectiveness of the proposed algorithms, a 9-bus system is defined by MATLAB software and also defining different line lengths and line characteristics in different buses. Moreover, after applying single-phase, two-phase, and three-phase faults, as well as presenting the results, fault localization is detected in the shortest time.

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“…The comparison is based on features of already published literature, including load balancing, resource management, and energy usage reduction. Almost all publications emphasize the significance of load balancing, except [23,27] and certain works that allude to resource management, including [19,20,22,23,25]. Almost all of these studies have not covered the idea of lowering energy usage and scheduling.…”

Section: Comparative Analysismentioning

confidence: 99%

Energy Consumption Reduction Strategy and a Load Balancing Mechanism for Cloud Computing in IoT Environment

Zhang¹,

Li²

2022

IJACSA

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Modern networks are built to be linked, agile, programmable, and load-efficient in order to overcome the drawbacks of an unbalanced network, such as network congestion, elevated transmission costs, low reliability, and other problems. The many technological devices in our environment have a considerable potential to make the connected world concept a reality. The Internet of Things (IoT) is a research community initiative to bring this idea to life. Cloud computing is crucial to making it happen. The load balancing and scheduling significantly increase the possibility of using resources and provide the grounds for reliability. Even if the intended node is under low or high loading, the load balancing techniques can increase its efficiency. This paper presents a scheduling technique for optimal resource allocation with enhanced particle swarm optimization and virtual machine live migration technique. The proposed technique prevents excessive or low server overloads through optimal allocation and scheduling tasks to physical servers. The proposed strategy was implemented in the cloudsim simulator environment and compared and showed that the proposed method is more effective and is well suited to decreasing execution time and energy consumption. This solution provides grounds to reduce energy consumption in the cloud environment while decreasing execution time. The simulation results showed that the amount of energy consumption compared to particle crowding has decreased by 10% and compared to PSO (Particle Swarm Optimization) scheduling by more than 8%. Also, the execution time has been reduced by 18% compared to particle swarm scheduling and by 8% compared to PSO.

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Blocking-Resilient Communications in Information-Centric Networks Using Router Redirection

Cited by 11 publications

References 12 publications

Offering a Demand‐Based Charging Method Using the GBO Algorithm and Fuzzy Logic in the WRSN for Wireless Power Transfer by UAV

Offering a Demand‐Based Charging Method Using the GBO Algorithm and Fuzzy Logic in the WRSN for Wireless Power Transfer by UAV

A New Method for Fault Detection and Location in a Low-Resistance Grounded Power Distribution Network Using Voltage Phasor of D-PMUs Data

Energy Consumption Reduction Strategy and a Load Balancing Mechanism for Cloud Computing in IoT Environment

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