Document Type



Doctor of Philosophy (PhD)


Electrical Engineering

First Advisor's Name

Niki Pissinou

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Sundaraja Sitharama Iyengar

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Jean H. Andrian

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Kang K. Yen

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Deng Pan

Fifth Advisor's Committee Title

Committee Member


Mobile Networks, Resource Constraint Networks, Mobile Wireless Sensor Networks, Economic-based Data Handling, Data Cleaning, Data Trend Prediction, Distributed Data Cleaning, Economic Theories

Date of Defense



The applications that employ mobile networks depend on the continuous input of reliable data collected by sensing devices. A common application is in military systems, where as an example, drones that are sent on a mission can communicate with each other, exchange sensed data, and autonomously make decisions. Although the mobility of nodes enhances the network coverage, connectivity, and scalability, it introduces pressing issues in data reliability compounded by restrictions in sensor energy resources, as well as limitations in available memory, and computational capacity.

This dissertation investigates the issues that mobile networks encounter in providing reliable data. Our research goal is to develop a diverse set of novel data handling solutions for mobile sensor systems providing reliable data by considering the dynamic trajectory behavior relationships among nodes, and the constraints inherent to mobile nodes. We study the applicability of economic models, which are simplified versions of real-world situations that let us observe and make predictions about economic behavior, to our domain. First, we develop a data cleaning method by introducing the notion of “beta,” a measure that quantifies the risk associated with trusting the accuracy of the data provided by a node based on trajectory behavior similarity. Next, we study the reconstruction of highly incomplete data streams. Our method determines the level of trust in data accuracy by assigning variable “weights” considering the quality and the origin of data. Thirdly, we design a behavior-based data reduction and trend prediction technique using Japanese candlesticks. This method reduces the dataset to 5% of its original size while preserving the behavioral patterns. Finally, we develop a data cleaning distribution method for energy-harvesting networks. Based on the Leontief Input-Output model, this method increases the data that is run through cleaning and the network uptime.



Previously Published In

  1. Concepcion Sanchez Aleman, Niki Pissinou, Sheila Alemany, Kianoosh Boroojeni, Jerry Miller, and Ziqian Ding. Context-Aware Data Cleaning for Mobile Wireless Sensor Networks: A Diversified Trust Approach. In 2018 International Conference on Computing, Networking & Communications (ICNC), 2018.
  2. Concepcion Sanchez Aleman, Niki Pissinou, Sheila Alemany, and Georges A. Kamhua. A Dynamic Trust Weight Allocation Technique for Data Reconstruction in Mobile Wireless Sensor Networks. In 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), pp. 61-67. IEEE, 2018.
  3. Concepcion Sanchez Aleman, Niki Pissinou, Sheila Alemany, and Georges A. Kamhoua. Using Candlestick Charting and Dynamic Time Warping for Data Behavior Modeling and Trend Prediction for MWSN in IoT. In 2018 IEEE International Conference on Big Data (Big Data), pp. 2884-2889. IEEE, 2018.
  4. Concepcion Sanchez Aleman, Niki Pissinou, and Sheila Alemany. Leontief-Based Data Cleaning Workload Distribution Strategy for EH-MWSN. In 2020IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR), pp. 1-6. IEEE, 2020.



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