Electrical and Computer Engineering ETDs

Publication Date

Fall 12-16-2023


In this thesis, an energy efficient task offloading mechanism in a multi-access edge computing (MEC) environment is introduced, based on the principles of Contract Theory. The technology of Reconfigurable Intelligent Surfaces (RIS) is adopted and serves as the enabler for the energy efficient task offloading, from the perspective of location-awareness and improved communication environment. Initially a novel positioning, navigation, and timing solution is designed, based on the RIS technology and an artificial intelligent method that selects a set of RISs to perform the multilateration technique and determine the Internet of Things (IoT) nodes’ positions in an efficient and accurate manner is introduced. Being aware of the nodes’ positions, a maximization problem of the nodes’ sum received signal strength at the MEC server where the nodes offload their computing tasks is formulated and solved, determining each RIS element’s optimal phase shifts. Capitalizing on these enhancements, a contract-theoretic task offloading mechanism is devised enabling the MEC server to incentivize the IoT nodes to offload their tasks to it for further processing in an energy efficient manner, while accounting for the improved nodes’ communications and computing characteristics. The performance evaluation of the proposed framework is obtained via modeling and simulation under different operation scenarios.


RIS, MEC, PNT, IoT, Reinforcement learning, Contract theory

Document Type




Degree Name

Computer Engineering

Level of Degree


Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Eirini Eleni Tsiropoulou

Second Committee Member

Jim Plusquellic

Third Committee Member

Symeon Papavassiliou