Physical Sciences and Mathematics Commons^™

Management Of Data Brokers In Support Of Smart Community Applications, Shadha Tabatabai

Dissertations

The widespread use of smart devices has led to the Internet of Things (IoT) revolution. Big data generated by billions of devices must be analyzed to make better decisions. However, this introduces security, communication, and processing problems. To solve these problems, we develop algorithms to enhance the work of brokers. We focus our efforts on three problems.

In the first problem, brokers are used in the cloud along with Software Defined Network (SDN) switches. We formulate minimizing brokers’ load difference within a reconfiguration budget with the constraint of indivisible topics as an Integer Linear Programming (ILP) problem. We show that …

Protecting Privacy Of Data In The Internet Of Things With Policy Enforcement Fog Module, Abduljaleel Al-Hasnawi

Dissertations

The growth of IoT applications has resulted in generating massive volumes of data about people and their surroundings. Significant portions of these data are sensitive since they reflect peoples' behaviors, interests, lifestyles, etc. Protecting sensitive IoT data from privacy violations is a challenge since these data need to be handled by public networks, servers and clouds, most of which are untrusted parties for data owners. In this study, a solution called Policy Enforcement Fog Module (PEFM) is proposed for protecting sensitive IoT data. The primary task of the PEFM solution is mandatory enforcement of privacy polices for sensitive IoT data-whenever …

Exploring The Role Of Semi-Supervised Deep Reinforcement Learning And Ensemble Methods In Support Of The Internet Of Things, Mehdi Mohammadi

Dissertations

Smart services are an important element of the Internet of Things (IoT) ecosystem where insights are drawn from raw data through the use of machine learning techniques. However, the pathway to develop IoT smart services is complicated as IoT data presents several challenges for machine learning, including handling big data, shortage of labeled data, and the need to benefit from the spatio-temporal relations hidden in the training data.

In this dissertation, after reviewing the state-of-the-art deep learning (DL) and deep reinforcement learning (DRL) techniques and their use in support of IoT applications, this study proposes to extend DRL to semi-supervised …