Document Type



Doctor of Philosophy (PhD)


Computer Science

First Advisor's Name

S. S. Iyengar

First Advisor's Committee Title

Co-committee Chair

Second Advisor's Name

Niki Pissinou

Second Advisor's Committee Title

Co-committee Chair

Third Advisor's Name

Kang K. Yen

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Deng Pan

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Leonardo J. Bobadilla

Fifth Advisor's Committee Title

Committee Member


Internet of Things, Data Mining, Context-aware, Data Reliability, Reputation, Trajectory, Mobile Computing

Date of Defense



Internet of Things (IoT) is a critically important technology for the acquisition of spatiotemporally dense data in diverse applications, ranging from environmental monitoring to surveillance systems. Such data helps us improve our transportation systems, monitor our air quality and the spread of diseases, respond to natural disasters, and a bevy of other applications. However, IoT sensor data is error-prone due to a number of reasons: sensors may be deployed in hazardous environments, may deplete their energy resources, have mechanical faults, or maybe become the targets of malicious attacks by adversaries. While previous research has attempted to improve the quality of the IoT data, they are limited in terms of better realization of the sensing context and resiliency against malicious attackers in real time. For instance, the data fusion techniques, which process the data in batches, cannot be applied to time-critical applications as they take a long time to respond. Furthermore, context-awareness allows us to examine the sensing environment and react to environmental changes. While previous research has considered geographical context, no related contemporary work has studied how a variety of sensor context (e.g., terrain elevation, wind speed, and user movement during sensing) can be used along with spatiotemporal relationships for online data prediction.

This dissertation aims at developing online methods for data prediction by fusing spatiotemporal and contextual relationships among the participating resource-constrained mobile IoT devices (e.g. smartphones, smart watches, and fitness tracking devices). To achieve this goal, we first introduce a data prediction mechanism that considers the spatiotemporal and contextual relationship among the sensors. Second, we develop a real-time outlier detection approach stemming from a window-based sub-trajectory clustering method for finding behavioral movement similarity in terms of space, time, direction, and location semantics. We relax the prior assumption of cooperative sensors in the concluding section. Finally, we develop a reputation-aware context-based data fusion mechanism by exploiting inter sensor-category correlations. On one hand, this method is capable of defending against false data injection by differentiating malicious and honest participants based on their reported data in real time. On the other hand, this mechanism yields a lower data prediction error rate.



Previously Published In

  • Samia Tasnim, Mohammad Ataur Rahman Chowdhury, Kishwar Ahmed, Niki Pissinou, and S Sitharama Iyengar. Location aware code offloading on mobile cloud with qos constraint. In 2014 IEEE 11th Consumer Communications and Networking Conference (CCNC), pages 74-79. IEEE, 2014.
  • Samia Tasnim, Niki Pissinou, and SS Iyengar. A novel cleaning approach of environmental sensing data streams. In Consumer Communications & Networking Conference (CCNC), 2017 14th IEEE Annual, pages 632-633. IEEE, 2017.
  • Samia Tasnim, Juan Caldas, Niki Pissinou, SS Iyengar, and Ziqian Ding. Semantic-aware clustering-based approach of trajectory data stream mining. In 2018 International Conference on Computing, Networking and Communications (ICNC), pages 88-92. IEEE, 2018.
  • Samia Tasnim, Niki Pissinou, SS Iyengar, Abdur Shahid, et al. Reputation-aware data fusion and malicious participant detection in mobile crowdsensing. In 2018 IEEE International Conference on Big Data (Big Data), pages 4820-4828. IEEE, 2018.

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Creative Commons Attribution 4.0 License
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