SGER: Energy-Efficient FPGA Security Mechanisms for Large-Scale Sensor Networks

Washington State University, Pullman WA

Investigators

Abstract

Sensor networks are fundamentally resource-constrained in terms of size, battery, cost, and infrastructure, but are attractive for a wide variety of applications such as battlefield surveillance, environmental monitoring, emergency response to name a few. Providing secure communication is a challenge in deploying these networks. Current techniques implement security mechanisms independent of the energy levels at the nodes, potentially deplete energy at the nodes, create network partitions, and shorten network lifetime. Hardware implementations for cryptographic algorithms have been proposed to reduce computational load in these networks. This research focuses on secure communication for sensor networks that is also energy-efficient. The intellectual merit of the proposed research is to incorporate energy-efficient and secure schemes in combination with FPGA hardware implementations specific to sensor networks, the first attempt of its kind, and quantitatively measure the impact of the encryption/decryption on both security and energy consumption. The investigator team has knowledge and expertise of both hardware implementation and sensor network technology. This research has two major thrusts: (i) FPGA encryption/decryption implementation and (ii) network organization to support the FPGA security mechanism and improve energy-efficiency. The performance of the proposed research will be analyzed by evaluating the security and energy-efficiency. The broader impacts of this research spans from hardware implementations through algorithm development to promoting participation of graduate and undergraduate students. The proposed research will greatly improve the design and implementation of security features of sensor networks. Knowledge acquired in this research will be transmitted to students at graduate and undergraduate level by incorporating as projects and lectures in relevant courses. Graduate and undergraduate students will be involved in various aspects of proposed research. The PIs mentor graduate students from underrepresented groups and women and actively recruit, retain, and graduate them and will continue this in the future.