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This research work presents an exploratory study to find answers for managing traffic and payloads in edge/fog zone of the networks. Initial deployments have shown that edge computing has helped in fine grain customization of services and have produced better experiences for the users. However, at the same time, it also invites challenges such as overload, underload and imbalance in utilization of resources such as bandwidth, time responses, throughput etc. The main reason is highly constrained environment and restricted hardware capabilities of fog devices. The conventional algorithms, that work on finding the best routing paths for distributing and processing tasks, do not suffice with respect to Quality of Service (QoS) parameters of fog zone as their computations become unstable. An alternative approach is considered by adding software defined components, that can help in load balancing and managing the payloads using link analysis at the inter-nodes of cloud and fog zone. In this research work, a fuzzy load balancer is devised using different levels of design and tuning of fuzzy controls. This fuzzy logic based algorithm has been implemented for conducting link analysis as interconnects for managing traffic. The analysis shows that 3-level design is energy efficient for load balancing in fog zone due to reduced number of intervals in fuzzy design, reduced overhead in provisioning and improved responsiveness. Higher levels (5-level and 7-level) lead to creation of redundant fuzzy rules and wastage of resources. [ABSTRACT FROM AUTHOR]

In today's world, data leakage on computer systems, Internet of Things (IoT) devices, or mobile devices carriages a significant threat due to weaker encryption or communication techniques, resulting in the loss of data items. Identifying the leakage of sensitive data during data transmission requires an appropriate technique. In the IoT environment, default permissions granted to devices often lead to data leakage. This proposed method offers data leakage security based on data sensitivity. However, classifying sensitive data is challenging due to its large volume and data transformation. The proposed technique utilizes the minimum redundancy and maximum relevancy (mRMR) technique for feature selection. It accurately detects confidential data better than existing state-of-the-art methods and can identify rephrased confidential contents using filter-based features for sequential data leak deterrence. Specifically, data leakage measurement can be achieved using DBSCAN (Density-based Spatial Clustering of Applications with Noise) to average F-statistical values measured over individual time steps and to ensure continuity between leakage data points. [ABSTRACT FROM AUTHOR]