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Abstract Flow-based microfluidic biochips (FMBs) are widely used in biomedical research and diagnostics. However, their security against potential material-level cyber-physical attacks remains inadequately explored, posing a significant future challenge. One of the main components, polydimethylsiloxane (PDMS) microvalves, is pivotal to FMBs' functionality. However, their fabrication, which involves thermal curing, makes them susceptible to chemical tampering-induced material degradation attacks. Here, we demonstrate one such material-based attack termed “BioTrojans,” which are chemically tampered and optically stealthy microvalves that can be ruptured through low-frequency actuations. To chemically tamper with the microvalves, we altered the associated PDMS curing ratio. Attack demonstrations showed that BioTrojan valves with 30:1 and 50:1 curing ratios ruptured quickly under 2 Hz frequency actuations, while authentic microvalves with a 10:1 ratio remained intact even after being actuated at the same frequency for 2 days (345,600 cycles). Dynamic mechanical analyzer (DMA) results and associated finite element analysis revealed that a BioTrojan valve stores three orders of magnitude more mechanical energy than the authentic one, making it highly susceptible to low-frequency-induced ruptures. To counter BioTrojan attacks, we propose a security-by-design approach using smooth peripheral fillets to reduce stress concentration by over 50% and a spectral authentication method using fluorescent microvalves capable of effectively detecting BioTrojans.

Abstract Wearable Internet of Things (WIoT) and Artificial Intelligence (AI) are rapidly emerging technologies for healthcare. These technologies enable seamless data collection and precise analysis toward fast, resource-abundant, and personalized patient care. However, conventional machine learning workflow requires data to be transferred to the remote cloud server, which leads to significant privacy concerns. To tackle this problem, researchers have proposed federated learning, where end-point users collaboratively learn a shared model without sharing local data. However, data heterogeneity, i.e., variations in data distributions within a client (intra-client) or across clients (inter-client), degrades the performance of federated learning. Existing state-of-the-art methods mainly consider inter-client data heterogeneity, whereas intra-client variations have not received much attention. To address intra-client variations in federated learning, we propose a federated clustered multi-domain learning algorithm based on ClusterGAN, multi-domain learning, and graph neural networks. We applied the proposed algorithm to a case study on stress-level prediction, and our proposed algorithm outperforms two state-of-the-art methods by 4.4% in accuracy and 0.06 in the F1 score. In addition, we demonstrate the effectiveness of the proposed algorithm by investigating variants of its different modules.

Precise and dynamic manipulation of nano-objects on a large scale has been challenging. Here, the authors introduce acoustoelectronic nanotweezers, combining precision of electronic tweezers with large-field dynamic control of acoustic tweezers, demonstrating complex patterning of sub-100 nm objects.

Testing is regarded as one of the most difficult challenges for three-dimensional integrated circuits (3D ICs). In this paper, we want to optimize the cost of TAM (test access mechanism) and the test time for 3D IC. We used both greedy and simulated annealing algorithms to solve this optimization problem. We compare the results of two assumptions: soft-die mode and hard-die mode. The former assumes that the DfT of dies cannot be changed, while the latter assumes that the DfT of dies can be adjusted. The results show that thermal-aware cooptimization is essential to decide the optimal TAM and test schedule. Blindly adding TAM cannot reduce the total test cost due to temperature constraints. Another conclusion is that soft-die mode is more effective than hard-die mode to reduce the total test cost for 3D IC.

We present a two-tier distributed hash table-based scheme for data-collection in event-driven wireless sensor networks. The proposed method leverages mobile sinks to significantly extend the lifetime of the sensor network. We propose localized algorithms using a distributed geographic hash-table mechanism that adds load balancing capabilities to the data-collection process. We address the hotspot problem by rehashing the locations of the mobile sinks periodically. The proposed mobility model moves the sink node only upon the occurrence of an event according to the evolution of current events, so as to minimize the energy consumption incurred by the multihop transmission of the event-data. Data is collected via single-hop routing between the sensor node and the mobile sink. Simulation results demonstrate significant gains in energy savings, while keeping the latency and the communication overhead at low levels for a variety of parameter values.

We propose a novel data-delivery method for delay-sensitive traffic that significantly reduces the energy consumption in wireless sensor networks without reducing the number of packets that meet end-to-end real-time deadlines. The proposed method, referred to as SensiQoS, leverages the spatial and temporal correlation between the data generated by events in a sensor network and realizes energy savings through application-specific in-network aggregation of the data. SensiQoS maximizes energy savings by adaptively waiting for packets from upstream nodes to perform in-network processing without missing the real-time deadline for the data packets. SensiQoS is a distributed packet scheduling scheme, where nodes make localized decisions on when to schedule a packet for transmission to meet its end-to-end real-time deadline and to which neighbor they should forward the packet to save energy. We also present a localized algorithm for nodes to adapt to network traffic to maximize energy savings in the network. Simulation results show that SensiQoS improves the energy savings in sensor networks where events are sensed by multiple nodes, and spatial and/or temporal correlation exists among the data packets. Energy savings due to SensiQoS increase with increase in the density of the sensor nodes and the size of the sensed events.