Flying Ad-Hoc Networks (FANETs) are groups of UAVs connected in an Ad-Hoc manner to accomplish a common mission. The widespread acceptance of UAVs due to their low cost and high efficiency has attracted malicious security attacks against them. These attacks cannot be easily prevented due to UAVs’ limited computational power, short battery life, and inability to execute complex algorithms. FANETs rely on the Global Positioning System (GPS) for localization. GPS Spoofing, an easy-to-launch attack, is one of the main challenges in the FANET environment. In which, the legitimate and not-encrypted civilian GPS signals are overridden by counterfeit signals to deceive the UAVs to collide or to be hijacked. Researchers proposed various countermeasures to address GPS Spoofing attacks. To further assist future research, this paper provides a systematic literature review on GPS Spoofing attacks in FANETs and various defense mechanisms. After formulating specific research questions and applying well-defined inclusion/exclusion criteria and quality assessment tools, 70 research articles were extracted. The existing defense mechanisms were classified based on their objectives, namely, detection, mitigation, and/or prevention. This paper further classifies the prior research works based on the basis of defense mechanisms into those that rely on readings at various devices/sensors on drones, process the signals received by various sensors, defend against attacks employing various machine learning algorithms, rely on game theory, and leverage cryptographic techniques to authenticate and protect the confidentiality of GPS signals received by the drones. The extracted articles that proposed various defense mechanisms were analyzed to identify the attacker models, impacts of the attack, and detection performance. This study found that most of the proposed methods are detection approaches, rather than mitigation or prevention. It is noticed that almost all papers used simulation experiments rather than a proof-of-concept implementation, which does not demonstrate a convincing performance under more realistic mobility and propagation models. Furthermore, most solutions addressed GPS Spoofing for a single UAV. Only eight articles addressed multiple UAV scenarios and none of them provided a proof-of-concept evaluation.

Mobile Edge Computing (MEC) has been gaining a major interest for use in Cyber-Physical Systems (CPS) for Disaster Response and Tactical applications. These CPS generate a very large amount of mission-critical and personal data that require resilient and secure storage and sharing. In this article, we present the design, implementation, and evaluation of a framework for resilient data storage and sharing for MEC in CPS targeting the aforementioned applications. Our framework is built on the resiliency of three main components: EdgeKeeper, which ensures resilient coordination of the framework’s components; RSock, which provides resilient communication among CPS’s nodes; and R-Drive/R-Share which, leveraging EdgeKeeper and RSock, provides resilient data storage and sharing. EdgeKeeper employs a set of replicas and a consensus protocol for storing critical meta-data and ensuring fast reorganization of the CPS; RSock decides an optimal degree for replicating data that is communicated over lossy links. R-Drive employs an adaptive erasure-coded and encrypted resilient data storage; R-Share, leveraging RSock provides resilient peer-to-peer data sharing. We implemented our proposed framework on rapidly deployable systems (e.g., manpacks, testMobile Edge Clouds) and on Android devices, and integrated it with existing MEC applications. Performance evaluation results from three real-world deployments show that our framework provides resilient data storage and sharing in MEC for CPS.