Use this URL to cite or link to this record in EThOS:
Title: Energy-efficient optimal control for real-time computing systems
Author: Thammawichai, Mason
ISNI:       0000 0004 5917 761X
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2016
Availability of Full Text:
Access from EThOS:
Access from Institution:
Moving toward ubiquitous Cyber-Physical Systems - where computation, control and communication units are mutually interacting - this thesis aims to provide fundamental frameworks to address the problems arising from such a system, namely the real-time multiprocessor scheduling problem (RTMSP) and the multi-UAV topology control problem (MUTCP). The RTMSP is concerned with how tasks can be scheduled on available computing resources such that no task misses a deadline. An optimization-based control method was used to solve the problem. Though it is quite natural to formulate the task assignment problem as a mixed-integer nonlinear program, the computation cost is high. By reformulating the scheduling problem as a problem of first determining a percentage of task execution time and then finding the task execution order, the computation complexity can be reduced. Simulation results illustrate that our methods are both feasibility optimal and energy optimal. The framework is then extended to solve a scheduling problem with uncertainty in task execution times by adopting a feedback approach. The MUTCP is concerned with how a communication network topology can be determined such that the energy cost is minimized. An optimal control framework to construct a data aggregation network is proposed to optimally trade-off between communication and computation energy. The benefit of our network topology model is that it is a self-organized multi-hop hierarchical clustering network, which provides better performance in term of energy consumption, reliability and network scalability. In addition, our framework can be applied to both homogeneous and heterogeneous mobile sensor networks due to the generalization of the network model. Two multi-UAV information gathering applications, i.e. target tracking and area mapping, were chosen to test the proposed algorithm. Based on simulation results, our method can save up to 40% energy for a target tracking and 60% for an area mapping compared to the baseline approach.
Supervisor: Kerrigan, Eric Sponsor: Government of Thailand
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral