An Adaptive Temporal Convolutional Network Autoencoder for Malicious Data Detection in Mobile Crowd Sensing.

Mobile crowdsensing (MCS) systems rely on the collective contribution of sensor data from numerous mobile devices carried by participants. However, the open and participatory nature of MCS renders these systems vulnerable to adversarial attacks or data poisoning attempts where threat actors can inject malicious data into the system. There is a need for a detection system that mitigates malicious sensor data to maintain the integrity and reliability of the collected information. This paper addresses this issue by proposing an adaptive and robust model for detecting malicious data in MCS scenarios involving sensor data from mobile devices. The proposed model incorporates an adaptive learning mechanism that enables the TCN-based model to continually evolve and adapt to new patterns, enhancing its capability to detect novel malicious data as threats evolve. We also present a comprehensive evaluation of the proposed model's performance using the SherLock datasets, demonstrating its effectiveness in accurately detecting malicious sensor data and mitigating potential threats to the integrity of MCS systems. Comparative analysis with existing models highlights the performance of the proposed TCN-based model in terms of detection accuracy, with an accuracy score of 98%. Through these contributions, the paper aims to advance the state of the art in ensuring the trustworthiness and security of MCS systems, paving the way for the development of more reliable and robust crowdsensing applications.

Optimization of Glutaraldehyde Vapor Treatment for Electrospun Collagen/Silk Tissue Engineering Scaffolds.

Freestanding fibrous matrices with proper protein composition and desirable mechanical properties, stability, and biocompatibility are in high demand for tissue engineering. Electrospun (E-spun) collagen-silk composite fibers are promising tissue engineering scaffolds. However, as-spun fibers are mechanically weak and unstable. In this work, we applied glutaraldehyde (GA) vapor treatment to improve the fiber performance, and the effect on the properties of E-spun collagen-silk fibers was studied systematically. GA treatment was found to affect collagen and silk distinctively. Whereas GA chemically links collagen peptides, it induces conformational transitions to enrich ß-sheets in silk. The combined effects impose a control of the mechanical properties, stability, and degradability of the composite fibers, which are dependent on the extent of GA treatment. In addition, a mild treatment of the fibers did not diminish cell proliferation and viability. However, overly treated fibers demonstrated reduced cell-matrix adhesion. The understanding of GA treatment effects on collagen, silk, and the composite fibers enables effective control and fine tuning of the fiber properties to warrant their diverse in vitro and in vivo applications.