Hybrid cuckoo–red deer algorithm for multiobjective localization strategy in wireless sensor network

Summary For the past 5 years, localization has become an emerging area in the field of wireless sensor networks (WSNs), which can be applicable for diverse applications, such as outdoor environments and monitoring of objects located in indoor environments. The major constraint in localization is all...

Full description

Saved in:
Bibliographic Details
Published in:International journal of communication systems Vol. 35; no. 4
Main Authors: Agoramoorthy, Moorthy, Praveen Joe, Irudaya Raj
Format: Journal Article
Language:English
Published: Chichester Wiley Subscription Services, Inc 10.03.2022
Subjects:
Accuracy
Algorithms
Convergence
Deer
distance function
energy
Energy costs
Heuristic methods
hybrid cuckoo–red deer algorithm
Indoor environments
Information retrieval
Localization
multiobjective function
node localization
Nodes
received signal strength
Search algorithms
Sensors
Signal strength
Wireless networks
Wireless sensor networks
ISSN:1074-5351, 1099-1131
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Summary For the past 5 years, localization has become an emerging area in the field of wireless sensor networks (WSNs), which can be applicable for diverse applications, such as outdoor environments and monitoring of objects located in indoor environments. The major constraint in localization is allocating a location for each node as diverse sensor nodes are employed to retrieve the information in WSN. Recent research works in node localization have generally moved by multihop range‐free localization approaches for achieving high accuracy through reducing the localization error among the actual and estimated position of nodes. The conventional approaches are aimed to improve the localization accuracy without considering efficiency concerned with algorithm's convergence time and energy costs. The main intent of this paper is to model the WSN localization model, which focuses on the localization of anchor nodes concerning the target nodes. The multiobjective function is developed by considering the distance function, received signal strength (RSS), and energy. Here, a hybrid metaheuristic algorithm named hybrid cuckoo–red deer algorithm (HC‐RDA) with the integration of red deer algorithm (RDA) and cuckoo search algorithm (CSA) is adopted for localization of unknown nodes. The fitness function of the developed model is the minimization of the derived multiobjective function. Both simulation studies and theoretical analysis demonstrate that the proposed approach can improve localization performance with a better convergence rate, thus ensuring high localization accuracy compared to conventional metaheuristic models. An RSN is a remote system consisting of spatially dispersed autonomous contraptions that employ sensors to screen regular or physical conditions. These center points or autonomous contraptions have solidified with a gateway and switches for making a suitable WSN model. The estimation centers utilize a central pathway for communication that offers a correlation among the wired world. The processes like assembling, planning, examining, and demonstrating the data have been done.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1074-5351
1099-1131
DOI:10.1002/dac.5042