Summary Perpetual lifetimes and low maintenance are few of the attractive aspects of sensor nodes that harvest ambient energy. However, their operation depends heavily on the energy profiles of their harvesting source(s). In this work, we study the suitability of energy harvesting sensor (EHS) Nodes, powered using indoor lighting and vibrations, for a simple temperature monitoring application. To help these nodes sustain, we have proposed schemes that allow the nodes to sample and transmit data judiciously. Along with an adaptive sampling based on autoregressive (AR) model, we have proposed a regulating function based transmission scheme that regulates the amount of transmitted data based on the energy available at the node and the characteristics of the data so that even with limited transmissions the fidelity of the data is not lost. With the help of thorough evaluations, we can conclude that an EHS node fares quite well. Results show that adaptive sampling and transmissions based on regulating function not only save energy at the sensor nodes, but they also reduce the amount of data generated and accumulated in a network.
The supply chain traceability of components from a production facility to deployment and maintenance depends upon its irrefutable identity. There are two well-known methods for identification which includes an identity code stored in the memory and embedding a custom identification hardware. While storing the identity code is susceptible to malicious and unintentional attacks, the approach of embedding a custom identification hardware is infeasible for sensor nodes assembled with Commercially-Off-the-Shelf (COTS) devices. We propose a novel identifier - Acoustic PUF based on the innate properties of the sensor node. Acoustic PUF combines the uniqueness component and the position component of the sensor device signature. The uniqueness component is derived by exploiting the manufacturing tolerances, thus making the signature unclonable. The position component is derived through acoustic fingerprinting, thus giving a sticky identity to the sensor device. We evaluate Acoustic PUF for Uniqueness, Repeatability and Position identity with a deployment spanning several weeks. Through our experimental evaluation and further numerical analysis, we prove that Acoustic PUF can uniquely identify thousands of devices with 99% accuracy while simultaneously detecting the change in position.
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