A Centralized Strategy for Multi-Agent Exploration

Gul, Faiza; Mir, Adnan; Mir, Imran; Mir, Suleman; Islaam, Tauqeer Ul; Abualigah, Laith; Forestiero, Agostino

doi:10.1109/access.2022.3218653

Cited by 30 publications

(20 citation statements)

References 64 publications

(79 reference statements)

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“…In this section, we discuss the configuration and fog cloud for the proposed system. The parameters settings are given in Table 2 [ 42 , 43 , 44 ].…”

Section: Experimental Evaluationmentioning

confidence: 99%

AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application

Mohamed

Abualigah

Alburaikan

et al. 2023

Sensors

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Recently, the concept of the internet of things and its services has emerged with cloud computing. Cloud computing is a modern technology for dealing with big data to perform specified operations. The cloud addresses the problem of selecting and placing iterations across nodes in fog computing. Previous studies focused on original swarm intelligent and mathematical models; thus, we proposed a novel hybrid method based on two modern metaheuristic algorithms. This paper combined the Aquila Optimizer (AO) algorithm with the elephant herding optimization (EHO) for solving dynamic data replication problems in the fog computing environment. In the proposed method, we present a set of objectives that determine data transmission paths, choose the least cost path, reduce network bottlenecks, bandwidth, balance, and speed data transfer rates between nodes in cloud computing. A hybrid method, AOEHO, addresses the optimal and least expensive path, determines the best replication via cloud computing, and determines optimal nodes to select and place data replication near users. Moreover, we developed a multi-objective optimization based on the proposed AOEHO to decrease the bandwidth and enhance load balancing and cloud throughput. The proposed method is evaluated based on data replication using seven criteria. These criteria are data replication access, distance, costs, availability, SBER, popularity, and the Floyd algorithm. The experimental results show the superiority of the proposed AOEHO strategy performance over other algorithms, such as bandwidth, distance, load balancing, data transmission, and least cost path.

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“…In this section, we discuss the configuration and fog cloud for the proposed system. The parameters settings are given in Table 2 [ 42 , 43 , 44 ].…”

Section: Experimental Evaluationmentioning

confidence: 99%

AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application

Mohamed

Abualigah

Alburaikan

et al. 2023

Sensors

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“…Gul et al [ 42 ] proposed a hybrid architecture for multi-robot space exploration by integrating the CME algorithm with the AO method. The architecture begins with a deterministic CME calculation of the cost and utility of the robot’s adjacent cells.…”

Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

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Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.

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“…Therefore, it is likely that the performance enhancement of future wireless networks is difficult to achieve with conventional mathematical solutions. The application of machine learning (ML) has been gaining traction across a range of industries, including robotics, image processing, healthcare, finance, and transportation [18][19][20][21][22]. In [18], a hybrid of deterministic and swarm-based algorithms was applied for multi-robot exploration in a cluttered environment.…”

Section: Introductionmentioning

confidence: 99%

“…The application of machine learning (ML) has been gaining traction across a range of industries, including robotics, image processing, healthcare, finance, and transportation [ 18 , 19 , 20 , 21 , 22 ]. In [ 18 ], a hybrid of deterministic and swarm-based algorithms was applied for multi-robot exploration in a cluttered environment. In [ 19 ], a self-organized and self-healing peer-to-peer information system was designed for a dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

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The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

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A Centralized Strategy for Multi-Agent Exploration

Cited by 30 publications

References 64 publications

AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application

AOEHO: A New Hybrid Data Replication Method in Fog Computing for IoT Application

A Comprehensive Survey on Aquila Optimizer

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

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