Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm. (11th March 2020)

• Record Type: Journal Article
• Authors: Sadeghi, Fahimeh; Avokh, Avid
• Is Part Of: International journal of communication systems. Volume 33:Number 9(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-11
• Subjects: clustering -- cuckoo‐search algorithm -- data aggregation -- Internet of Things -- network lifetime; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.4385
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: This paper deals with the lifetime problem in the Internet of Things. We first propose an efficient cluster‐based scheme named "Cuckoo‐search Clustering with Two‐hop Routing Tree (CC‐TRT)" to develop a two‐hop load‐balanced data aggregation routing tree in the network. CC‐TRT uses a modified energy‐aware cuckoo‐search algorithm to fairly select the best cluster head (CH) for each cluster. The applied cuckoo‐search algorithm makes the CH role to rotate between different sensors round by round. Subsequently, we extend the CC‐TRT scheme to present two methods for constructing multi‐hop data aggregation routing trees, named "Cuckoo‐search Clustering with Multi‐Hop Routing Tree (CC‐MRT)" and "Cuckoo‐search Clustering with Weighted Multi‐hop Routing Tree (CC‐WMRT)." Both CC‐MRT and CC‐WMRT rely on a two‐level structure; they not only use an energy‐aware cuckoo‐search algorithm to fairly select the best CHs but also adopt a load‐balanced high‐level routing tree to route the aggregated data of CHs to the sink node. However, CC‐WMRT slightly has a better performance thanks to its low‐level routing strategy. As an advantage, the proposed schemes balance the energy consumption among different sensors. Numerical results show the efficiency of the CC‐TRT, CC‐MRT, and CC‐WMRT algorithms in terms of the number of transmissions, remaining energy, energy consumption variance, and network lifetime. Abstract : This paper presents three energy‐aware algorithms, named CC‐TRT, CC‐MRT, and CC‐WMRT, to improve the network lifetime. We introduce a routing method in the form of a two‐level hybrid architecture, including the low‐level routing tree and high‐level routing tree. A modified cuckoo‐search algorithm with an efficient fitness function is used to select the best cluster head for each cluster. Extensive simulations show the efficiency of the proposed algorithms in terms of the number of transmissions, remaining energy, energy consumption variance, and network lifetime.