Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.765917
Title: Cluster heads selection and cooperative nodes selection for cluster-based Internet of Things networks
Author: Song, Liumeng
ISNI:       0000 0004 7652 6703
Awarding Body: Queen Mary University of London
Current Institution: Queen Mary, University of London
Date of Award: 2017
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Abstract:
Clustering and cooperative transmission are the key enablers in power-constrained Internet of Things (IoT) networks. The challenges for power-constrained devices in IoT networks are to reduce the energy consumption and to guarantee the Quality of Service (QoS) provision. In this thesis, optimal node selection algorithms based on clustering and cooperative communication are proposed for different network scenarios, in particular: • The QoS-aware energy efficient cluster heads (CHs) selection algorithm in one-hop capillary networks. This algorithm selects the optimum set of CHs and construct clusters accordingly based on the location and residual energy of devices. • Cooperative nodes selection algorithms for cluster-based capillary networks. By utilising the spacial diversity of cooperative communication, these algorithms select the optimum set of cooperative nodes to assist the CHs for the long-haul transmission. In addition, with the regard of evenly energy distribution in one-hop cluster-based capillary networks, the CH selection is taken into consideration when developing cooperative devices selection algorithms. The performance of proposed selection algorithms are evaluated via comprehensive simulations. Simulation results show that the proposed algorithms can achieve up to 20% network lifetime longevity and up to 50% overall packet error rate (PER) decrement. Furthermore, the simulation results also prove that the optimal tradeoff between energy efficiency and QoS provision can be achieved in one-hop and multi-hop cluster-based scenarios.
Supervisor: Not available Sponsor: Chinese Scholarship Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.765917  DOI: Not available
Keywords: Electronic Engineering and Computer Science ; Clustering ; cooperative transmission ; Internet of Things
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