An energy-efficient artificial bee colony-based clustering in the internet of things. (September 2020)

• Record Type: Journal Article
• Authors: Yousefi, Shamim; Derakhshan, Farnaz; Aghdasi, Hadi S.; Karimipour, Hadis
• Is Part Of: Computers & electrical engineering. Volume 86(2020)
• Publication Date: 2020-09
• Subjects: Artificial bee colony (ABC) algorithm -- Clustering -- Data transmission delay -- Energy efficiency -- Internet of things (IoT) -- Wireless communication; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106733
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Improving the tradeoff between energy consumption and transmission delay in IoT. Exploiting artificial bee colony to select the efficient cluster-heads in IoT. Considering energy, neighbors and distance as the criteria for cluster-head selection. Providing an artificial bee colony-based mechanism for clustering devices efficiently. Considering distance and data volume as the criteria for clustering IoT devices. Abstract: Wireless communication on the Internet of Things (IoT) requires context-aware data transmission protocols. Developing an energy-efficient clustering mechanism is the primary challenge in data transmission over IoT. The existing approaches struggle with the short lifetime of IoT, imbalance load distribution, and high transmission delay. This paper proposes a novel cluster-head selection and clustering mechanism on IoT. It is composed of two main phases. The first phase selects the near-optimal cluster-heads using Artificial Bee Colony (ABC) algorithm. Performance criteria include the residual energy of the devices, the number of neighbors, Euclidean distance between devices and the sink, and Euclidean distance between each device and its neighbors. The principal objective of the second phase is to group devices into some clusters based on Euclidean distance between each cluster-head and its members, and the data volume generated by clusters. Simulation results verify that our mechanism improves energy consumption, lifetime, and transmission delay. Graphical abstract: Image, graphical abstract