Theses and Dissertations


Issuing Body

Mississippi State University



Committee Member

Gurbuz, Ali C.

Committee Member


Committee Member


Date of Degree


Original embargo terms

Campus Access Only 1 Year

Document Type

Dissertation - Campus Access Only


Electrical and Computer Engineering

Degree Name

Doctor of Philosophy (Ph.D)


James Worth Bagley College of Engineering


Department of Electrical and Computer Engineering


Wireless sensor networks (WSNs) have become pivotal in precision agriculture, environmental monitoring, and smart healthcare applications. However, the challenges of energy consumption and security, particularly concerning the reliance on large battery-operated nodes, pose significant hurdles for these networks. Energy-harvesting wireless sensor networks (EH-WSNs) emerged as a solution, enabling nodes to replenish energy from the environment remotely. Yet, the transition to EH-WSNs brought forth new obstacles in ensuring reliable and secure data transmission.

In our initial study, we tackled the intermittent connectivity issue prevalent in EH-WSNs due to the dynamic behavior of energy harvesting nodes. Rapid shifts between ON and OFF states led to frequent changes in network topology, causing reduced link stability. To counter this, we introduced the hybrid routing method (HRM), amalgamating grid-based and opportunistic-based routing. HRM incorporated a packet fragmentation mechanism and cooperative localization for both static and mobile networks. Simulation results demonstrated HRM's superior performance, enhancing key metrics such as throughput, packet delivery ratio, and energy consumption in comparison to existing energy-aware adaptive opportunistic routing approaches.

Our second research focused on countering emerging threats, particularly the malicious energy attack (MEA), which remotely powers specific nodes to manipulate routing paths. We developed intelligent energy attack methods utilizing Q-learning and Policy Gradient techniques. These methods enhanced attacking capabilities across diverse network settings without requiring internal network information. Simulation results showcased the efficacy of our intelligent methods in diverting traffic loads through compromised nodes, highlighting their superiority over traditional approaches.

In our third study, we developed a deep learning-based two-stage framework to detect MEAs. Utilizing a stacked residual network (SR-Net) for global classification and a stacked LSTM network (SL-Net) to pinpoint specific compromised nodes, our approach demonstrated high detection accuracy. By deploying trained models as defenses, our method outperformed traditional threshold filtering techniques, emphasizing its accuracy in detecting MEAs and securing EH-WSNs.

In summary, our research significantly advances the reliability and security of EH-WSN, particularly focusing on enhancing the network layer. These findings offer promising avenues for securing the future of wireless sensor technologies.