Enhancing security of mobile crowd sensing in unmanned aerial vehicle ecosystems

The rapid expansion of mobile devices with enhanced sensing and computing capabilities has driven the growth of mobile crowd sensing (MCS), enabling applications that collect large datasets from sources like smartphones and smartwatches. However, this data aggregation raises substantial security and privacy concerns, especially when MCS integrates with unmanned aerial vehicles (UAVs), where potential risks are further amplified. This study identifies and analyzes specific security and privacy threats in UAV-based MCS through the framework of the confidentiality, integrity, and availability (CIA) triad. We categorize potential vulnerabilities and propose comprehensive countermeasures targeting hardware, software, and communication models. Our findings outline strategic and actionable countermeasures to mitigate identified risks, thus ensuring data integrity and reliable functionality within MCS systems. Additionally, we present a security scenario involving mitigation suggested for data integrity and recovery. This work underscores the critical need for robust security frameworks in UAV-enhanced MCS applications, offering a holistic approach to mitigate emerging security threats.