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Opportunistic networks utilize pocket switching for routing where each node forwards its messages to a suitable next node. The selection of the forwarder node is crucial for the efficient performance of a routing protocol. In any opportunistic network, some nodes have a paramount role in the routing process and these nodes could be identified with the assistance of the existing centrality measures available in network theory. However, the central nodes tend to suffer from congestion because a large number of nodes that are relatively less central attempt to forward their payload to the central nodes to increase the probability of the message delivery. This paper evaluates mechanisms to transform the social encounters into congestion aware metrics so that high-ranking central nodes are downgraded when they encounter congestion. The network transformations are aimed at aggregating the connectivity patterns of the nodes to implicitly accumulate the network information to be utilized by centrality measures for routing purposes. We have analyzed the performance of the metrics' computed centrality measures using routing simulation on three real-world network traces. The results revealed that betweenness centrality along with the congestion aware network metrics holds the potential to deliver a competitive number of messages. Additionally, the proposed congestion aware metrics significantly balance the routing load among the central nodes. [ABSTRACT FROM AUTHOR]

Nowadays, embedded systems are comprised of heterogeneous multi-core architectures, i.e., CPUs and GPUs. If the application is mapped to an appropriate processing core, then these architectures provide many performance benefits to applications. Typically, programmers map sequential applications to CPU and parallel applications to GPU. The task mapping becomes challenging because of the usage of evolving and complex CPU- and GPU-based architectures. This paper presents an approach to map the OpenCL application to heterogeneous multi-core architecture by determining the application suitability and processing capability. The classification is achieved by developing a machine learning-based device suitability classifier that predicts which processor has the highest computational compatibility to run OpenCL applications. In this paper, 20 distinct features are proposed that are extracted by using the developed LLVM-based static analyzer. In order to select the best subset of features, feature selection is performed by using both correlation analysis and the feature importance method. For the class imbalance problem, we use and compare synthetic minority over-sampling method with and without feature selection. Instead of hand-tuning the machine learning classifier, we use the tree-based pipeline optimization method to select the best classifier and its hyper-parameter. We then compare the optimized selected method with traditional algorithms, i.e., random forest, decision tree, Naïve Bayes and KNN. We apply our novel approach on extensively used OpenCL benchmarks, i.e., AMD and Polybench. The dataset contains 653 training and 277 testing applications. We test the classification results using four performance metrics, i.e., F-measure, precision, recall and R 2 . The optimized and reduced feature subset model achieved a high F-measure of 0.91 and R 2 of 0.76. The proposed framework automatically distributes the workload based on the application requirement and processor compatibility. [ABSTRACT FROM AUTHOR]