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Fog computing has the capability to provide computing resources to end-to-end devices like Internet-of-Things (IoT), thereby reducing the burden on the cloud. However, due to the growth of IoT devices and an increase in resource consumption, load balancing in fog computing has turned into a challenging task, since improper load allocation may result in underutilization and overutilization while transferring the tasks from one node to another. In order to solve these challenges, we presented an Energy-efficient load balancing algorithm named Hybrid Priority Assigned Laxity (HPAL) algorithm that allocates the tasks to a suitable Virtual Machine (VM) and completes the task within the minimum time. After the task allocation, the load balancing is handled by calculating the fog optimal time and minimum execution time. Response Time (RT), Processing Time (PT), Delay Time (DT), Execution Time (ET) and Energy Consumption (EC) are the five factors considered in this work to design an energy-efficient load balancing in Fog Nodes (FNs). The proposed algorithm carries two phases, among which in the first phase the task is allocated to each VM according to priority within the fog optimal time and in the second phase, the reallocation of tasks is executed within the minimum execution time considering the energy factor. Therefore, the task migration between the FNs is handled in an energy-efficient manner without affecting the lifetime of the FNs. [ABSTRACT FROM AUTHOR]

Cloud computing plays a vital role in processing a large amount of data. However, with the arrival of the Internet of Things, huge data are generated from these devices. Thus, there is the need for bringing characteristics of cloud closer to the request generator, so that processing of these huge data takes place at one-hop distance closer to that end user. This led to the emergence of fog computing with the aim to provide storage and computation at the edge of the network that reduces network traffic and overcomes many cloud computing drawbacks. Fog computing technology helps to overcome challenges of big data processing. The paper discusses the taxonomy of fog computing, how this is different from cloud computing and edge computing technologies, its applications, emerging key technologies (i.e., communication technologies and storage technologies) and various challenges involved in fog technology. [ABSTRACT FROM AUTHOR]

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]