Date of Award

8-2020

Degree Name

Doctor of Philosophy

Department

Computer Science

First Advisor

Dr. Ala Al-Fuqaha

Second Advisor

Dr. Ajay Gupta

Third Advisor

Dr. Alvis Fong

Fourth Advisor

Dr. Mohammad Salahuddin

Keywords

Internet of Things, resource optimization, online and real-time algorithms, federated learning, message ferries, knapsack and secretary problems

Abstract

With the rise of the Internet of Things (IoT) and smart communities, managing computation and communication resources required by billions of smart devices becomes a concern. To tackle this problem, we develop algorithms for resource management to ensure better Quality of Service (QoS), safety, and performance. We focus our efforts on three problems.

In the first problem, we studied the strict QoS requirements of applications and differentiated service requirements in different situations of vehicular networks. We propose a generic prioritization and resource management algorithm that can be used to prioritize the processing of received packets in vehicular networks. We formulate the generic severity-based prioritized packet processing problem as Penalized Multiple Knapsack Problem (PMKP) and prove that it is an NP-Hard problem. We thus develop a real-time heuristic that utilizes a relaxed version of the formulation. The relaxed formulation executes in polynomial time and guarantees a minimum delay per severity-level while respecting the processing rate constraint. To measure the performance of the proposed heuristic, real traffic data is used in a small-scale experiment. The proposed heuristic is tested against the PMKP solution and results show a small degradation of up to 4% in profit for the heuristic compared to the PMKP solution. Also, the proposed heuristic is tested against a non-prioritized processing algorithm that works using first come first served policy. Results show that the proposed heuristic gains 9% to 67% more profit than the non-prioritized processing algorithm in moderate and high congestion scenarios.

In the second problem, we explored the utilization of existing vehicles on roads as “message ferries” for the transport data for smart community applications to avoid the cost of installing new communication infrastructure. We propose an opportunistic data ferry selection algorithm that strives to select vehicles that can minimize the overall delay for data delivery from a source to a given destination. Our proposed opportunistic algorithm utilizes an ensemble of online hiring algorithms, which are run together in passive mode, to select the online hiring algorithm that has performed the best in recent history. The proposed ensemble-based algorithm is evaluated empirically using real-world traces from taxies plying routes in Shanghai, China, and its performance is compared against a baseline of four stateof- the-art online hiring algorithms. A number of experiments are conducted and our results indicate that the proposed algorithm can reduce the overall delay compared to the baseline by an impressive 13% to 258%.

In the third problem, we solve the problem of optimizing accuracy in stateful federated learning with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process. We formulate the problem of maximizing the probability of selecting the best candidate clients based on test accuracy as a secretary problem then analytically analyze the performance and provide proofs. Next, we propose an online stateful FL heuristic to find the best candidate clients. Additionally, we propose an IoT client alarm application that utilizes the proposed heuristic in training a stateful FL global model based on IoT device classification to alert clients about unauthorized IoT devices in their environment. To test the efficiency of the proposed online heuristic, we conduct several experiments using a real dataset and compare the results of the proposed online heuristic against state-of-the-art algorithms. Our results indicate that the proposed heuristic performance is comparable to the performance of the best offline algorithm and outperforms the online random algorithm with up to 27% gain in accuracy.

Access Setting

Dissertation-Campus Only

Restricted to Campus until

8-31-2022

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