We expect a future where we are surrounded by embedded devices, ranging from Java-enabled cell phones to sensor networks and smart appliances. An adversary can compromise our privacy and safety by maliciously modifying the memory contents of these embedded devices. In this paper, we propose a SoftWare-based ATTestation technique (SWATT) to verify the memory contents of embedded devices and establish the absence of malicious changes to the memory contents. SWATT does not need physical access to the device's memory, yet provides memory content attestation similar to TCG or NGSCB without requiring secure hardware. SWATT can detect any change in memory contents with high probability, thus detecting viruses, unexpected configuration settings, and Trojan Horses. To circumvent SWATT, we expect that an attacker needs to change the hardware to hide memory content changes.We present an implementation of SWATT in off-the-shelf sensor network devices, which enables us to verify the contents of the program memory even while the sensor node is running.

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As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases1–3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

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Abstract-Effective employment of piezoelectric actuators in microscale dynamic trajectory-tracking applications is limited by two factors: 1) the intrinsic hysteretic behavior of piezoelectric ceramic and 2) structural vibration as a result of the actuator's own mass, stiffness, and damping properties. While hysteresis is rate-independent, structural vibration increases as the piezoelectric actuator is driven closer to its resonant frequency. Instead of separately modeling the two interacting dynamic effects, this work treats their combined effect phenomenologically and proposes a rate-dependent modified Prandtl-Ishlinskii operator to account for the hysteretic nonlinearity of a piezoelectric actuator at varying actuation frequency. It is shown experimentally that the relationship between the slope of the hysteretic loading curve and the rate of control input can be modeled by a linear function up to a driving frequency of 40 Hz.