No abstract

We report a simple and efficient electrical sensing scheme that can be used to overcome the “diffusion limit” of affinity-based biosensors by incorporating the structural advantage of a concentric electrode biosensor platform and the microstirring effect of AC electrothermal flow (ACEF). To prove the effect of ACEF on the biosensor performance, we performed both simulations and experiments for the detection of cardiac troponin-I, which is a biomarker for acute myocardial infarction. The finite element simulation results indicate that AC bias to the electrode (which has a concentric structure in our device) can induce fast convection flow, which facilitates the transport of the target molecules to the binding region located between the two electrodes. In our device, the channel region made of a carbon nanotube network decorated with gold nanoparticles, which act as the attaching sites of the probe molecules, is used as a highly sensitive electrical channel. We find that the electrical sensing method exhibited extremely fast sensing speeds compared with those under no bias (diffusion-limited) conditions.

Edge computing offers a promising paradigm for implementing the industrial Internet of things (IIoT) by offloading intensive computing tasks from resource constrained machine type devices to powerful edge servers. However, efficient spectrum resource management is required to meet the quality of service requirements of various applications, taking into account the limited spectrum resources, batteries, and the characteristics of available spectrum fluctuations. Therefore, this study proposes intelligent dynamic spectrum resource management consisting of learning engines that select optimal backup channels based on history data, reasoning engines that infer idle channels based on backup channel lists, and transmission parameter optimization engines based genetic algorithm using interference analysis in time, space and frequency domains. The performance of the proposed intelligent dynamic spectrum resource management was evaluated in terms of the spectrum efficiency, number of spectrum handoff, latency, energy consumption, and link maintenance probability according to the backup channel selection technique and the number of IoT devices and the use of transmission parameters optimized for each traffic environment. The results demonstrate that the proposed method is superior to existing spectrum resource management functions.

No abstract

No abstract