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ORCID
https://orcid.org/0000-0003-3807-7102
Access Type
Campus-Only Access for One (1) Year
Document Type
thesis
Embargo Period
1-17-2023
Degree Program
Electrical & Computer Engineering
Degree Type
Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)
Year Degree Awarded
2023
Month Degree Awarded
February
Abstract
Timely data collection and execution in heterogeneous Internet of Things (IoT) networks in which different protocols and spectrum bands coexist such as WiFi, RFID, Zigbee, and LoRa, requires further investigation. This thesis studies the problem of age-of-information minimization in heterogeneous IoT networks consisting of heterogeneous IoT devices, an intermediate layer of multi-protocol mobile gateways (M-MGs) that collects and relays data from IoT objects and performs computing tasks, and heterogeneous access points (APs). A federated matching framework is presented to model the collaboration between different service providers (SPs) to deploy and share M-MGs and minimize the average weighted sum of the age-of-information and energy consumption. Further, we develop a two-level multi-protocol multi-agent actor-critic (MP-MAAC) to solve the optimization problem, where M-MGs and SPs can learn collaborative strategies through their own observations. The M-MGs' strategies include selecting IoT objects for data collection, execution, relaying, and/or offloading to SPs’ access points while SPs decide on spectrum allocation. Finally, to improve the convergence of the learning process we incorporate federated learning into the multi-agent collaborative framework. The numerical results show that our Fed-Match algorithm reduces the AoI by factor four, collects twice more packets than existing approaches, reduces the penalty by factor five when enabling relaying, and establishes design principles for the stability of the training process.
First Advisor
Beatriz Lorenzo
Recommended Citation
Xia, Xiaohao, "Heterogeneous IoT Network Architecture Design for Age of Information Minimization" (2023). Masters Theses. 1281.
https://scholarworks.umass.edu/masters_theses_2/1281