The Internet of Things is the networking of electronic devices, or “Things”, that enables them to collect and share data, as well as interact with their physical surround- ings. Analyzing this collected data allows us to make smarter economic decisions. These interconnected networks are usually driven by low-powered micro-controllers or cheap CPUs that are designed to function optimally with very little hardware. As scale and computational requirements increase, these micro-controllers are unable to grow without being physically replaced.
This thesis proposes a system, IoTA, that assists the Internet of Things by pro- viding a shared computational resource for endpoint devices. This solution extends the functionality of endpoint devices without the need of physical replacement. The IoTA system is designed to be easily integrable to any existing IoT network.
This system presents a model that allows for seamless processing of jobs submitted by endpoint devices while keeping scalability and flexibility in mind. Additionally, IoTA is built on top of existing IoT protocols. Evaluation shows there is a significant performance benefit in processing computationally heavy algorithms on the IoTA system as compared to processing them locally on the endpoint devices themselves.
","abstract_html":"<p>The Internet of Things is the networking of electronic devices, or “Things”, that enables them to collect and share data, as well as interact with their physical surround- ings. Analyzing this collected data allows us to make smarter economic decisions. These interconnected networks are usually driven by low-powered micro-controllers or cheap CPUs that are designed to function optimally with very little hardware. As scale and computational requirements increase, these micro-controllers are unable to grow without being physically replaced.</p> <p>This thesis proposes a system, IoTA, that assists the Internet of Things by pro- viding a shared computational resource for endpoint devices. This solution extends the functionality of endpoint devices without the need of physical replacement. The IoTA system is designed to be easily integrable to any existing IoT network.</p> <p>This system presents a model that allows for seamless processing of jobs submitted by endpoint devices while keeping scalability and flexibility in mind. Additionally, IoTA is built on top of existing IoT protocols. Evaluation shows there is a significant performance benefit in processing computationally heavy algorithms on the IoTA system as compared to processing them locally on the endpoint devices themselves.</p>","abstract_has_math":false,"creators":["Okumura, Brandon M"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Foaad Khosmood"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-07-01T07:00:00Z","date_published":"2017-07-01T07:00:00Z","updated_at":"2026-07-24T01:31:56Z","subjects":["IoT","Internet","network","offloading","CoAP","Computational Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2017.71"],"render_values":[{"text":"10.15368/theses.2017.71","href":"https://doi.org/10.15368/theses.2017.71","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1769","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Foaad Khosmood"]},{"key":"dc:creator","label":"Author","values":["Okumura, Brandon M"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2017-07-27T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["IoT","Internet","network","offloading","CoAP","Computational Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1769","10.15368/theses.2017.71"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The Internet of Things is the networking of electronic devices, or “Things”, that enables them to collect and share data, as well as interact with their physical surround- ings. Analyzing this collected data allows us to make smarter economic decisions. These interconnected networks are usually driven by low-powered micro-controllers or cheap CPUs that are designed to function optimally with very little hardware. As scale and computational requirements increase, these micro-controllers are unable to grow without being physically replaced.
This thesis proposes a system, IoTA, that assists the Internet of Things by pro- viding a shared computational resource for endpoint devices. This solution extends the functionality of endpoint devices without the need of physical replacement. The IoTA system is designed to be easily integrable to any existing IoT network.
This system presents a model that allows for seamless processing of jobs submitted by endpoint devices while keeping scalability and flexibility in mind. Additionally, IoTA is built on top of existing IoT protocols. Evaluation shows there is a significant performance benefit in processing computationally heavy algorithms on the IoTA system as compared to processing them locally on the endpoint devices themselves.
"]},{"key":"dc:title","label":"Title","values":["IoTA: Internet of Things Assistant"]}]}],"canonical_facts":{"dc:contributor":["Foaad Khosmood"],"dc:creator":["Okumura, Brandon M"],"dc:date.available":["2017-07-27T07:00:00Z"],"dc:description.abstract":["The Internet of Things is the networking of electronic devices, or “Things”, that enables them to collect and share data, as well as interact with their physical surround- ings. Analyzing this collected data allows us to make smarter economic decisions. These interconnected networks are usually driven by low-powered micro-controllers or cheap CPUs that are designed to function optimally with very little hardware. As scale and computational requirements increase, these micro-controllers are unable to grow without being physically replaced.
This thesis proposes a system, IoTA, that assists the Internet of Things by pro- viding a shared computational resource for endpoint devices. This solution extends the functionality of endpoint devices without the need of physical replacement. The IoTA system is designed to be easily integrable to any existing IoT network.
This system presents a model that allows for seamless processing of jobs submitted by endpoint devices while keeping scalability and flexibility in mind. Additionally, IoTA is built on top of existing IoT protocols. Evaluation shows there is a significant performance benefit in processing computationally heavy algorithms on the IoTA system as compared to processing them locally on the endpoint devices themselves.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1769","10.15368/theses.2017.71"],"dc:subject":["IoT","Internet","network","offloading","CoAP","Computational Engineering"],"dc:title":["IoTA: Internet of Things Assistant"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:31:56Z"}