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.

With the rise in cloud computing and virtualisation, secure and efficient VPN solutions are essential for network connectivity. We present a systematic performance comparison of OpenVPN (v2.6.12) and WireGuard (v1.0.20210914) across Azure and VMware environments, evaluating throughput, latency, jitter, packet loss, and resource utilisation. Testing revealed that the protocol performance is highly context dependent. In VMware environments, WireGuard demonstrated a superior TCP throughput (210.64 Mbps vs. 110.34 Mbps) and lower packet loss (12.35% vs. 47.01%). In Azure environments, both protocols achieved a similar baseline throughput (~280–290 Mbps), though OpenVPN performed better under high-latency conditions (120 Mbps vs. 60 Mbps). Resource utilisation showed minimal differences, with WireGuard maintaining slightly better memory efficiency. Security Efficiency Index calculations revealed environment-specific trade-offs: WireGuard showed marginal advantages in Azure, while OpenVPN demonstrated better throughput efficiency in VMware, though WireGuard remained superior for latency-sensitive applications. Our findings indicate protocol selection should be guided by deployment environment and application requirements rather than general superiority claims.