Fog computing (FC) is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network (close to the Internet of Things (IoT) devices). Fog nodes provide services in lieu of the cloud. Thus, improving the performance of the network and making it attractive to social media-based systems. Security issues are one of the most challenges encountered in FC. In this paper, we propose an anomalybased Intrusion Detection and Prevention System (IDPS) against Man-in-the-Middle (MITM) attack in the fog layer. The system uses special nodes known as Intrusion Detection System (IDS) nodes to detect intrusion in the network. They periodically monitor the behavior of the fog nodes in the network. Any deviation from normal network activity is categorized as malicious, and the suspected node is isolated. Exponentially Weighted Moving Average (EWMA) is added to the system to smooth out the noise that is typically found in social media communications. Our results (with 95% confidence) show that the accuracy of the proposed system increases from 80% to 95% after EWMA is added. Also, with EWMA, the proposed system can detect the intrusion from 0.25-0.5 s seconds faster than that without EWMA. However, it affects the latency of services provided by the fog nodes by at least 0.75-1.3 s. Finally, EWMA has not increased the energy overhead of the system, due to its lightweight.
This paper describes the design and implementation of a unique WSN platform specifically researched to monitor the health conditions of the vibration screens used by Oil Sand operators in Canada. Previous to WSN, wired sensing solutions have been attempted for this project, but failed to sustain integrity in the harsh conditions imposed by the environment. The researched platform allowed, for the first time, to monitor the thickness of the screen ligaments by providing real-time thickness measurements of the mesh screen. The architecture design of the platform was made modular and scalable to easily adopt the sensor platform for other industrial facilities making it flexible across other applications. A complete system was realized at Queen's University TRLab and successfully presented to the Oil Sand operator on a miniature working lab model.
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