Content Details | NUML Online Research Repository

Content Details

Back to Department Listing

Title Artificial Bee Colony based Optimization for Data Sharing in Internet of Things

Author(s) Anees Asghar

Abstract Internet of Things (IoT) comprises of complicated and dynamical aggregation of smart units that normally need decentralized command for data sharing across the networks. The most popular swarm intelligent techniques artificial bee colony (ABC) is inspired from collective actions of honey bees that can be used for solving problems during clustering in large scale data of IoT. The main problem is that each food source is compared with every other food source in neighborhood to determine the best global food source. It requires unnecessary comparisons to compare the pair of poor quality food sources as well. It results in consuming more utilization time, slow convergence speed and increased delay. This work presents an enhanced ABC (E-ABC) based optimization for data collection and replication mechanism. E-ABC improves the previous ABC algorithm by reducing the unnecessary comparisons. E-ABC compares the best source with the available sources which will excludes the comparison of poor resources. The proposed E-ABC algorithm was applied on replica selection to prove its supremacy as compared to counterparts in terms of convergence speed, data availability and response time. Results show the supremacy of proposed E-ABC over previous algorithms. The proposed algorithm provides 65% better response time from DCR2S and 20% better than MOABC when number of cloudlets are 1000. The file availability probability for E-ABC is noticed as 85% when total cost is 20. Additionally, some open research challenges are highlighted on the basis of literature which will help the researchers to find the research gap with respect to IoT and ABC.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

Publication Date 2021-07-05

Subject Computer Science

Publisher

Contributor(s)

Format

Identifier

Source

Relation

Coverage

Rights