Description
Smart cities are the next wave of rapid expansion of Internet of Things (IoT). A smart city is a designation given to a city that incorporates information and communication technologies (ICT) to enhance the quality and performance of urban services, such as energy, transportation, healthcare, communications, entertainments, education, e-commerce, businesses, city management, and utilities, to reduce resource consumption, wastage and overall costs. The overarching aim of a smart city is to enhance the quality of living for its residents and businesses, through technology. In a large ecosystem, like a smart city, many organizations and companies collaborate with the smart city government to improve the smart city. These entities may need to store and share critical data with each other. A smart city has several thousands of smart devices and sensors deployed across the city. Storing critical data in a secure and scalable manner is an important issue in a smart city. While current cloud-based services, like Splunk and ELK (Elasticsearch-Logstash-Kibana), offer a centralized view and control over the IT operations of these smart devices, it is still prone to insider attacks, data tampering, and rogue administrator problems. In this thesis, we present an approach using blockchain to recovering critical data from unauthorized modifications. We use extensive simulations based on complex adaptive system theory, for evaluation of our approach. Through mathematical proof we proved that the approach always detects an unauthorized modification of critical data.
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Details
Title
- An Approach to Recovery of Critical Data of Smart Cities Using Blockchain
Contributors
- Mishra, Vineeta (Author)
- Yau, Sik-Sang (Thesis advisor)
- Goul, Michael K (Committee member)
- Huang, Dijiang (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2017
Resource Type
Collections this item is in
Note
- Masters Thesis Computer Science 2017