ACOCA: Ant Colony Optimization Based Clustering Algorithm for Big Data Preprocessing

Singh, Neelam; Singh, Devesh Pratap; Pant, Bhasker

doi:10.33889/ijmems.2019.4.5-098

Cited by 17 publications

(4 citation statements)

References 21 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [ 64 ], an ACO-based clustering method (ACOCA) is suggested for massive data preprocessing. The hybrid algorithm can facilitate faster search by streamlining the procedure.…”

Section: Iot Challenges Faced By Simentioning

confidence: 99%

Swarm Intelligence to Face IoT Challenges

Abualigah

Falcone

Forestiero

2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The Internet of Things (IoT) paradigm denotes billions of physical entities connected to Internet that allow the collecting and sharing of big amounts of data. Everything may become a component of the IoT thanks to advancements in hardware, software, and wireless network availability. Devices get an advanced level of digital intelligence that enables them to transmit real-time data without applying for human support. However, IoT also comes with its own set of unique challenges. Heavy network traffic is generated in the IoT environment for transmitting data. Reducing network traffic by determining the shortest route from the source to the aim decreases overall system response time and energy consumption costs. This translates into the need to define efficient routing algorithms. Many IoT devices are powered by batteries with limited lifetime, so in order to ensure remote, continuous, distributed, and decentralized control and self-organization of these devices, power-aware techniques are highly desirable. Another requirement is to manage huge amounts of dynamically changing data. This paper reviews a set of swarm intelligence (SI) algorithms applied to the main challenges introduced by the IoT. SI algorithms try to determine the best path for insects by modeling the hunting behavior of the agent community. These algorithms are suitable for IoT needs because of their flexibility, resilience, dissemination degree, and extension.

show abstract

“…In [ 64 ], an ACO-based clustering method (ACOCA) is suggested for massive data preprocessing. The hybrid algorithm can facilitate faster search by streamlining the procedure.…”

Section: Iot Challenges Faced By Simentioning

confidence: 99%

Swarm Intelligence to Face IoT Challenges

Abualigah

Falcone

Forestiero

2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…There will undoubtedly be a ying species in the party that has excellent food detection abilities while the search is underway. As a result, the particles adjust their velocities in opposition to the local best and then converge on the global best location [20]. Therefore, PSO is used to obtain the optimal solution by combining the local best and the global best.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Implementation of Delay-Sensitive Smart Healthcare Framework in Cloud and Fog Environment

Rajpoot,

Singh,

Pant

2023

Preprint

View full text Add to dashboard Cite

Smart healthcare systems are novel innovations that can improve healthcare services by giving individuals details about their medical conditions in real time. However, processing and analyzing huge amounts of information stored in these systems can frequently result in latency and interruptions, negatively impacting the system's efficiency. To solve this issue, this research presents an innovative healthcare framework incorporating cloud and fog computing techniques to reduce latency and enhance whole system performance. The framework has three different tiers: the user tier, the fog tier, and the cloud tier. Health-related information is gathered from users in the user tier using cloud pulses. An optimized Ant Colony Optimization load balancing algorithm subsequently monitors this data. The load balancer allocates user requests to fog servers in the fog tier, considering factors including response time and cost. The fog tier is comprised of fog servers that are located in closer proximity to end-users and are tasked with the real-time processing and analysis of data. The cloud tier accommodates vast quantities of healthcare data and facilitates sophisticated analytics and processing functionalities. The performance of the proposed framework was assessed by implementing it on the Cloud Analyst tool and utilizing metrics such as response time, cost, and system throughput. The experiment findings indicate that the proposed framework performs better in mitigating delay in healthcare compared to conventional healthcare systems. The load balancer that was optimized using Ant Colony Optimization algorithm efficiently allocated the workload among the fog servers, resulting in enhanced system response time and decreased latency. The concept of fog computing proved to be highly productive in mitigating latency by enabling local data processing and analysis near the end-user, thereby reducing reliance on the cloud tier. The empirical findings indicate that the framework put forward can transform the provision of healthcare services, rendering them more streamlined, economical, and focused on the needs of patients.

show abstract

“…Metaheuristic approaches for the k-means and similar problems include genetic algorithms [46,103,104], the ant colony clustering hybrid algorithm proposed in [105], particle swarm optimization algorithms [106]. Almost all of these algorithms in one way or another use the Lloyd's procedure or other local search procedures.…”

Section: Of 32mentioning

confidence: 99%

Self-Adjusting Variable Neighborhood Search Algorithm for Near-Optimal k-Means Clustering

Kazakovtsev¹,

Rozhnov²,

Popov³

et al. 2020

Computation

View full text Add to dashboard Cite

The k-means problem is one of the most popular models in cluster analysis that minimizes the sum of the squared distances from clustered objects to the sought cluster centers (centroids). The simplicity of its algorithmic implementation encourages researchers to apply it in a variety of engineering and scientific branches. Nevertheless, the problem is proven to be NP-hard which makes exact algorithms inapplicable for large scale problems, and the simplest and most popular algorithms result in very poor values of the squared distances sum. If a problem must be solved within a limited time with the maximum accuracy, which would be difficult to improve using known methods without increasing computational costs, the variable neighborhood search (VNS) algorithms, which search in randomized neighborhoods formed by the application of greedy agglomerative procedures, are competitive. In this article, we investigate the influence of the most important parameter of such neighborhoods on the computational efficiency and propose a new VNS-based algorithm (solver), implemented on the graphics processing unit (GPU), which adjusts this parameter. Benchmarking on data sets composed of up to millions of objects demonstrates the advantage of the new algorithm in comparison with known local search algorithms, within a fixed time, allowing for online computation.

show abstract

ACOCA: Ant Colony Optimization Based Clustering Algorithm for Big Data Preprocessing

Cited by 17 publications

References 21 publications

Swarm Intelligence to Face IoT Challenges

Swarm Intelligence to Face IoT Challenges

Implementation of Delay-Sensitive Smart Healthcare Framework in Cloud and Fog Environment

Self-Adjusting Variable Neighborhood Search Algorithm for Near-Optimal k-Means Clustering

Contact Info

Product

Resources

About