| dc.contributor.author | Usman Shahid Khan, Muhammad | |
| dc.description.abstract | Recommender systems apply numerous knowledge discovery techniques to suggest the preferred products, information, or service on contextual data. In our study, we utilize the recommender system for analyzing and measuring the network dynamics. The dynamic factors such as change in network shape or data size affect the performance of the networks and make it harder for the optimization techniques to be used for finding the optimum solution. In our research, we focused on the monitoring and analyzing the dynamic factors involved in two networks: (a) body area networks and (b) road networks; and based on the study proposed the efficient solution for mitigating the negative effects of the dynamic factors involved using recommender systems. In body area networks, we monitored the dynamically changing body area sensors data and studied the correlation between the sensors’ location and activity recognition. We proposed a cloud based framework that has employed a feature descriptor called Local Energy-based Shape Histogram (LESH) to preserve the maximum information of local energy. We have also used the Wearable Action Recognition Database (WARD) dataset to perform the experiments. Based on our study we proposed the best combination of sensors for various activities recognition. In road networks, we monitored the congestion during large-scale emergency evacuation and proposed efficient route recommendation service that helps in fast and safe evacuation. To respond to emergencies in a fast and an effective manner, it is of critical importance to have efficient evacuation plans that lead to minimum road congestion. The existing approaches, mostly based on multi-objective optimizations, are not scalable enough when involve numerous time varying parameters, such as traffic volume, safety status, and weather conditions. In this study, we propose a new scalable emergency evacuation service that recommends the evacuees with the most preferred routes towards safe locations during a disaster. The evacuees are directed towards those routes that are safe and have least congestion resulting in decreased evacuation time. The results indicated the improved efficiency of our service for average evacuation times and evacuation delays. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU Policy 190.6.2 | |
| dc.title | Utilizing Recommender Systems as an Analysis Tool for Measuring Network Dynamics | en_US |
| dc.type | Dissertation | en_US |
| dc.type | Video | en_US |
| dc.date.accessioned | 2015-06-02T13:22:30Z | |
| dc.date.available | 2015-06-02T13:22:30Z | |
| dc.date.issued | 2015 | |
| dc.identifier.uri | http://hdl.handle.net/10365/24999 | |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | |
| ndsu.degree | Doctor of Philosophy (PhD) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Electrical and Computer Engineering | en_US |
| ndsu.program | Electrical and Computer Engineering | en_US |
| ndsu.advisor | Khan, Samee U. | |
