Salp Swarm Optimization-Based Clustering Algorithm (SSOCA) in Adaptive FANET to Improve QoS for Disaster Response Operations

Pandey, Ankur; Shukla, Piyush Kumar; Agrawal, Ratish

doi:10.1007/s11277-022-09842-4

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…It was discovered that the classifcation accuracy was much greater when compared to other conventional audio characteristics. Particularly at low frequencies [4], it was discovered that the GTCC was much more efective than the MFCC.…”

Section: Related Workmentioning

confidence: 99%

Content‐Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm

Samha

Alshammri

Nuagah

et al. 2023

Computational Intelligence and Neuroscience

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Audio classification and retrieval has been recognized as a fascinating field of endeavor for as long as it has existed due to the topic of identifying and choosing the most useful audio attributes. The categorization of audio files is significant not only in the area of multimedia applications but also in the disciplines of medicine, sound analysis, intelligent homes and cities, urban informatics, entertainment, and surveillance. This study introduces a new algorithm called the modified bacterial foraging optimization algorithm (MBFOA), which is based on a method that retrieves and classifies audio data. The goal of this algorithm is to reduce the computational complexity of existing techniques. Along with the combination of the peak estimated signal, the enhanced mel-frequency cepstral coefficient (EMFCC) and the enhanced power normalized cepstral coefficients (EPNCC) are used. These are then optimized using the fitness function utilizing MBFOA. The probabilistic neural network is used to differentiate between a music signal and a spoken signal from an audio source (PNN). It is next necessary to extract and list the characteristics that correspond to the class that was arrived at as a consequence of the categorization. When compared to other approaches that are somewhat similar, MBFOA demonstrates superior levels of sensitivity, specificity, and accuracy.

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Section: Related Workmentioning

confidence: 99%

Content‐Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm

Samha

Alshammri

Nuagah

et al. 2023

Computational Intelligence and Neuroscience

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“…Step Computational Intelligence and Neuroscience attacker makes requests to consume resources without providing any economic value for the host by taking advantage of the fact that they are aware of the behaviour of traffic patterns of the website that is now under attack. For introducing this form of traffic in Log L, the traffic is produced using the method mentioned above [27]. is traffic is created by simply replaying or starting to request the website's most popular pages to consume the bandwidth.…”

Section: An Assault Using a Session Replaymentioning

confidence: 99%

Detection of Malicious Cloud Bandwidth Consumption in Cloud Computing Using Machine Learning Techniques

Veeraiah

Mohanty

Kundu

et al. 2022

Computational Intelligence and Neuroscience

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The Internet of Things, sometimes known as IoT, is a relatively new kind of Internet connectivity that connects physical objects to the Internet in a way that was not possible in the past. The Internet of Things is another name for this concept (IoT). The Internet of Things has a larger attack surface as a result of its hyperconnectivity and heterogeneity, both of which are characteristics of the IoT. In addition, since the Internet of Things devices are deployed in managed and uncontrolled contexts, it is conceivable for malicious actors to build new attacks that target these devices. As a result, the Internet of Things (IoT) requires self-protection security systems that are able to autonomously interpret attacks in IoT traffic and efficiently handle the attack scenario by triggering appropriate reactions at a pace that is faster than what is currently available. In order to fulfill this requirement, fog computing must be utilised. This type of computing has the capability of integrating an intelligent self-protection mechanism into the distributed fog nodes. This allows the IoT application to be protected with the least amount of human intervention while also allowing for faster management of attack scenarios. Implementing a self-protection mechanism at malicious fog nodes is the primary objective of this research work. This mechanism should be able to detect and predict known attacks based on predefined attack patterns, as well as predict novel attacks based on no predefined attack patterns, and then choose the most appropriate response to neutralise the identified attack. In the environment of the IoT, a distributed Gaussian process regression is used at fog nodes to anticipate attack patterns that have not been established in the past. This allows for the prediction of new cyberattacks in the environment. It predicts attacks in an uncertain IoT setting at a speedier rate and with greater precision than prior techniques. It is able to effectively anticipate both low-rate and high-rate assaults in a more timely manner within the dispersed fog nodes, which enables it to mount a more accurate defence. In conclusion, a fog computing-based self-protection system is developed to choose the most appropriate reaction using fuzzy logic for detected or anticipated assaults using the suggested detection and prediction mechanisms. This is accomplished by utilising a self-protection system that is based on the development of a self-protection system that utilises the suggested detection and prediction mechanisms. The findings of the experimental investigation indicate that the proposed system identifies threats, lowers bandwidth usage, and thwarts assaults at a rate that is twenty-five percent faster than the cloud-based system implementation.

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“…Recently, the usage of unmanned aerial vehicles (UAVs) forming flying ad hoc networks (FANETs) or UAV networks has gained considerable attention from the industry and research community. Easy deployment, ability to fly, and high maneuverability made FANETs a practical solution for numerous applications related to military and civil, such as aerial surveillance, disaster management, agriculture, delivery systems, fire management, entertainment, and search and rescue (SAR) 1–4 . To meet the communication demands of various time‐critical FANET applications, a reliable routing approach is required to share data gathered by UAVs to the ground station (GS) in real‐time 5–8 .…”

Section: Introductionmentioning

confidence: 99%

MF‐DLB: Multimetric forwarding and directed acyclic graph‐based load balancing for geographic routing in FANETs

Singh,

Sharma,

Verma

2024

Concurrency and Computation

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SummaryFlying ad hoc network (FANET) comprising unmanned aerial vehicles (UAVs) emerges as a promising solution for numerous military and civil applications. Transferring data collected from the environment to the ground station (GS) is a primary concern for meeting the communication demands of most of these applications. However, the highly mobile UAVs with limited communication range, resulting in frequent topology change and intermittent connectivity, make data routing challenging. In such scenarios, geographic routing is a viable solution due to its scalability and robustness. However, the basic forwarding mechanism of geographic routing favors the neighboring UAV nearest to the destination, impacted substantially by link failures and routing holes in a dynamic environment. Additionally, routing decisions ignoring the current load over UAVs contribute to performance degradation due to the high concentration of data traffic near the GS. Thus, to address these issues, a geographic routing protocol named MF‐DLB comprising multimetric forwarding (MF) and a directed acyclic graph‐based load balancing (DLB) scheme is proposed to enhance packet forwarding in FANETs. MF takes account of multiple metrics related to connectivity, geographic progress, link lifetime, and residual energy to select the next hop with a stable communication link while effectively bypassing the routing holes. The second scheme, DLB, focuses on proactively maintaining routing paths near GS for load distribution among underutilized nodes to address the congestion problem. Simulations performed in network simulator ns‐3 confirm the outperformance of MF‐DLB over other related routing schemes in terms of different performance metrics.

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Salp Swarm Optimization-Based Clustering Algorithm (SSOCA) in Adaptive FANET to Improve QoS for Disaster Response Operations

Cited by 6 publications

References 22 publications

Content‐Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm

Content‐Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm

Detection of Malicious Cloud Bandwidth Consumption in Cloud Computing Using Machine Learning Techniques

MF‐DLB: Multimetric forwarding and directed acyclic graph‐based load balancing for geographic routing in FANETs

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