An energy-efficient artificial bee colony-based clustering in the internet of things

• Published in: Computers & electrical engineering Vol. 86; p. 106733
• Main Authors: Yousefi, Shamim, Derakhshan, Farnaz, Aghdasi, Hadi S., Karimipour, Hadis
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.09.2020; Elsevier BV
• Subjects: Algorithms; Artificial bee colony (ABC) algorithm; Clustering; Data transmission; Data transmission delay; Energy consumption; Energy efficiency; Euclidean geometry; Internet of Things; Internet of things (IoT); Load distribution (forces); Residual energy; Stress concentration; Swarm intelligence; Wireless communication; Wireless communications; Wireless communication; Artificial bee colony (ABC) algorithm; Energy efficiency; Clustering; Data transmission delay; Internet of things (IoT)
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2020.106733

•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. 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. [Display omitted]