Use this URL to cite or link to this record in EThOS:
Title: Computational intelligence algorithms for optimisation of wireless sensor networks
Author: Oladimeji, Muyiwa Olakanmi
ISNI:       0000 0004 6497 4015
Awarding Body: London South Bank University
Current Institution: London South Bank University
Date of Award: 2017
Availability of Full Text:
Access from EThOS:
Access from Institution:
Recent studies have tended towards incorporating Computation Intelligence, which is a large umbrella for all Machine Learning and Metaheuristic approaches into wireless sensor network (WSN) applications for enhanced and intuitive performance. Meta-heuristic optimisation techniques are used for solving several WSN issues such as energy minimisation, coverage, routing, scheduling and so on. This research designs and develops highly intelligent WSNs that can provide the core requirement of energy efficiency and reliability. To meet these requirements, two major decisions were carried out at the sink node or base station. The first decision involves the use of supervised and unsupervised machine learning algorithms to achieve an accurate decision at the sink node. This thesis presents a new hybrid approach for event (fire) detection system using k-means clustering on aggregated fire data to form two class labels (fire and non-fire). The resulting data outputs are trained and tested by the Feed Forward Neural Network, Naive Bayes, and Decision Trees classifier. This hybrid approach was found to significantly improve fire detection performance against the use of only the classifiers. The second decision employs a metaheuristic approach to optimise the solution of WSNs clustering problem. Two metaheuristic-based protocols namely the Dynamic Local Search Algorithm for Clustering Hierarchy (DLSACH) and Heuristics Algorithm for Clustering Hierarchy (HACH) are proposed to achieve an evenly balanced energy and minimise the net residual energy of each sensor nodes. This thesis proved that the two protocols outperforms state-of-the-art protocols such as LEACH, TCAC and SEECH in terms of network lifetime and maintains a favourable performance even under different energy heterogeneity settings.
Supervisor: Dudley, Sandra Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available