Intelligent Water Drops Algorithm-Based Aggregation in Heterogeneous Wireless Sensor Network

Sharma, Nonita; Xalikovich, Pardayev Abdunabi; Kumar, C. Ramesh; Rakhra, Manik; Samori, Issah Abubakari; Maquera, Yuselino Maquera; Arias-Gonzáles, José Luis

doi:10.1155/2022/6099330

Cited by 15 publications

(4 citation statements)

References 59 publications

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“…It is becoming increasingly clear that natural language processing (NLP) models can have a significant impact on higher education, with the potential to facilitate individualized learning, on-demand support, and other novel methods to instruction [12,13]. When it comes to helping students learn in higher education, NLP models are extremely useful.…”

Section: Emergence Of Ai In Educationmentioning

confidence: 99%

Exploring the ethical considerations of using Chat GPT in university education

Huallpa, et al.

2023

PEN

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This study investigates the moral dilemmas that arise with incorporating Chat GPT into higher education, with a focus on the situation in Latinoamerican institutions of higher learning. The study surveyed 220 people via online questionnaire to learn more about their experiences with and motivations for using AI-powered conversational agents. An overview of the demographics of the participants was provided through descriptive statistics. This investigation of the subject at hand lays the groundwork for further research. It also reveals the hidden meanings of the observed phenomena, and it suggests possible solutions to the problems that have been uncovered. This research looks at how AI systems and chatbots can supplement human knowledge and judgment, as well as their potential drawbacks. The results showed that participants thought Chat GPT integration was moderately accessible and had moderately positive social attitudes. They understood the value and responsibility of Chat GPT in creating individualized educational opportunities. Participants stressed the necessity for explicit institutional standards regarding privacy and data security. Gender, age, sense of accessibility, social attitude, opinions, and personal experience, privacy and data security, institutional guidelines, and individualized learning were also found to affect participants' reliance on AI through regression analysis. The findings shed light on how the integration of Chat GPT into Latinoamerican higher education is complicated by factors such as individual beliefs, cultural norms, and ethical problems. The busy schedules of students may be accommodated and the resources they need to succeed can be made available thanks to this adaptability. In addition, natural language processing models can offer students instantaneous help via text chat, voice, or video. To fully grasp the ethical consequences and lead the creation of responsible implementation techniques, the research proposes that additional qualitative investigations, longitudinal studies, and comparative research across diverse contexts is required. Closing these knowledge gaps will help move the conversational AI field forward in ways that are ethical and beneficial to the classroom.

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Section: Emergence Of Ai In Educationmentioning

confidence: 99%

Exploring the ethical considerations of using Chat GPT in university education

Huallpa, et al.

2023

PEN

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“…In this study, the algorithms have been categorized and grouped based on their search strategy and solution-oriented features to study the importance of workload balancing techniques by taking the data from articles published from 2012 to 2023. Examples of such algorithms encompass Simulated Annealing 31) , Tabu search 31) , Ant colony optimization [32][33] , Artificial Bee Colony/Honeybee [34][35] , Particle Swarm Optimization [36][37] , Artificial Bee Colony/Honeybee 37) , and Intelligent Water Drop (IWD) 38) .…”

Section: Fig 2: Taxonomy Of Meta-heuristic Techniquesmentioning

confidence: 99%

Innovative Analysis of Workload Balancing Algorithms for Fog-Cloud Networks: A Contemporary Perspective

Sohal,

Kait

2023

Evergreen

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Cloud technology is increasingly widespread as thriving businesses and research organizations seek to leverage its on-demand access, service models, and deployment patterns. However, further enhancements are still required to achieve optimal performance within this field. One challenging aspect is load balancing, specifically the equilibrium distribution of workload across virtual machines, which is a computationally complex problem. The present work extensively reviewed the state-of-the-art methods for load balancing in the cloud, encompassing traditional techniques, heuristics, meta-heuristics, and hybrid approaches. This paper provides a comprehensive historical assessment and comparative analysis of the prominent literature on load balancing, serving as a valuable resource for researchers aiming to develop new and effective load-balancing algorithms in the domain of fog-cloud networks. In this study, a thorough historical evaluation and comparative research of load-balancing literature can provide important insights into the development, efficacy, and adaptability of load-balancing strategies in a variety of scenarios. There are many fascinating directions that researchers in this area might go in, addressing both the past developments and the upcoming difficulties of load balancing in the dynamic environment of computing and networking.

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“…As a result, numerous routing protocols are implemented in WSN to lessen the quantity of transmission with the purpose of lowering the amount of power used. Data are pushed from the sensor node to the base station, which is the hub of a WSN, as its name indicates [3]. The data packet needs to travel farther and consumes more energy when the sensor node is far from the BS.…”

Section: Introductionmentioning

confidence: 99%

Learning Automata based Grey Wolf Optimization Algorithm for Adaptive data forwarding and Aggregation in Wireless Sensor Networks

Karmakonda,

Guguloth

2023

Preprint

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Reducing the packet count transmitted within a wireless sensor network is one method for decreasing the consumption of energy. In order to reduce the quantity of transmitted packets, many nodes may have substantially identical information and can be combined in intermediary nodes. Sensor networks typically deploy many redundant nodes to address the issue of node failures. When data packets from all nodes with same information are aggregated together, the aggregate ratio is maximized. Nodes should forward their packets along a path where there are as many nodes as possible that have information similar to that of the sender node. In many real-world scenarios, these path changes frequently and do not remain constant throughout the network lifetime. These changes, which typically cannot be anticipated in advance, may have occurred as a result of alterations to the environment of operating sensor network. In this study, a LA (learning automata) based solution to the aforementioned issue is given. For each node to broadcast its packets towards the sink, the learning automata collectively learn the path and choose the one with the highest aggregation ratio. Initially, the neighbor list of the sensor nodes is estimated using grey wolf optimization (GWO) technique with distance to sink & aggregation cost parameters to identify the paths with fewer count of hops to the sink. This proposed work utilized Adaptive forwarding (AF) factor in LA to determine intermediate nodes for data transmission. The decision about adaptive forwarding and aggregation based on the parameters: load factor & connectivity ratio was taken by LA configured in the sensor nodes makes. Performance assessments and analyses show that the suggested approach decreases energy usage, delays in data delivery, data transfers, and network lifespan.

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Intelligent Water Drops Algorithm-Based Aggregation in Heterogeneous Wireless Sensor Network

Cited by 15 publications

References 59 publications

Exploring the ethical considerations of using Chat GPT in university education

Exploring the ethical considerations of using Chat GPT in university education

Innovative Analysis of Workload Balancing Algorithms for Fog-Cloud Networks: A Contemporary Perspective

Learning Automata based Grey Wolf Optimization Algorithm for Adaptive data forwarding and Aggregation in Wireless Sensor Networks

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