Date of Graduation

Fall 2019

Degree

Master of Natural and Applied Science in Computer Science

Department

Computer Science

Committee Chair

Razib Iqbal

Keywords

cyber-physical systems, internet of things, operational policy, policy generation algorithms, associative behavioral rule generation, smart homes

Subject Categories

Other Computer Sciences | Theory and Algorithms

Abstract

The term “Cyber-Physical Systems” (CPS) refers to those systems which seamlessly integrate sensing, computation, control, and networking into physical objects and infrastructure [1]. In these systems, computers and networks of physical entities interact with each other to bring new capabilities to traditional physical systems. Since its introduction, the field of Cyber-Physical Systems (CPS) has evolved with new and interesting advancements concerning its capability, adaptability, scalability, and usability [1]. One such advancement is the unification of the Internet of Things (IoT), a concept that enables real-world everyday objects to connect to the internet and interact with each other, with CPS [1]. This emerging technology (CPS/IoT), however, comes with its own set of challenges. One such challenge is the need for new technologies to enhance the usability of CPS/IoT systems in smart homes by making it easier for users to manage and develop the core operating logic these systems employ. This need arose as smart home CPS/IoT systems have become more ubiquitous, with broader environments and more complex architectures and yet, they still rely on handwritten rules or hard coding the operational logic into the system. In light of this challenge, we introduce the concept of “operational policy”, a plan for the operation of a CPS/IoT system and frame the task of building the operational policy as a combinatorial optimization problem that algorithms can solve. We then introduce “Associative-Behavioral” rules, which constrain human input in the rule building process to selecting the actions they want their devices to take in a given scenario. From here, we introduce two algorithms for the generation of operational policy: an adaptation of the Differential Evolution algorithm and the A-Posteriori algorithm. These algorithms are compared with a brute force method of policy generation in a simulated CPS containing 60 devices with a focus on usability. We found that in a situation where there is previous information on the CPS, A-Posteriori performs the best. Whereas for the mid to large scale scenarios in our simulation without previous information, Differential Evolution performs the best. And finally, in small scale scenarios without previous information brute force was best.

Copyright

© Jared Wayne Hall

Open Access

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