Search

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.

Background: COVID-19 pandemic affected the operation of surgical services in Malaysia. Anxiety levels have been on the rise and could have possibly affected various health care providers. This study aims to determine whether an association is present between the amendments made to surgical practice and surgeon's anxiety level during this pandemic. Methods and Material: This study was conducted using a web-based anonymous survey via snowball sampling technique for 11 days. A total of 77 respondents from 22 different institutions participated in this study. Result: It was found that 70.1% surgeons experienced postponement of prior planned surgery during MCO period, while 26.3% surgeons had only some planned surgery being postponed and 2.6% stated MCO had not affected planned surgeries. Despite the uncertainties, the data showed that the majority (61.0%) of the surgeons had good control of anxiety, while 29.9% of the surgeons were mildly anxious, and 5.2% of the surgeons had moderate anxiety level. Only 3.9% of the respondents were at a severe stage of anxiety. A Chi-square test was conducted to determine the relationship between the postponement of elective surgery and the surgeon's level of anxiety, resulting in a statistically significant association between these two factors. Conclusion: It was concluded from the results that surgeons were significantly affected by the changes introduced to surgical practice during the pandemic. [ABSTRACT FROM AUTHOR]