In this paper, we evaluate the secrecy outage probability (SOP) of two user cooperative non-orthogonal multiple access (C-NOMA) network in the presence of a passive eavesdropper (E). The near user $(U_{1})$ harvests energy from the superimposed signal transmitted by source (S). The near user $(U_{1})$ acts as a relay and retransmits the message signal to a far user $(U_{2})$ . The E eavesdrops the signal for $U_{1}$ and $U_{2}$ transmitted from $\mathbf{S}$ and eavesdorp the signal of $U_{2}$ relayed from $U_{1}$ . An MRC is applied at E on two copies of signals obtained for $U_{2}$ . An analytical framework for evaluating SOP is presented. We obtain an analytical expression for SOP of $U_{1}$ , while an analytical expression of SOP of $U_{2}$ at high SNR approximation is obtained. Impact of several netwrok parameters such as target secrecy rate, energy harvesting parameters on SOP is indicated. Our analytical results are supported by monte-carlo simulation carried out in MATLAB.
Time variant coverage, called sweep coverage in wireless sensor networks has got attention from various re-searchers in recent time. In this problem, a set of mobile sensors are collectively monitoring certain area of interest (AoI). In various applications periodic monitoring of the AoI is sufficient. For a given time period, the objective of the sweep coverage problem is to find minimum number of mobile sensors so that the periodic monitoring can be guaranteed. This problem has been studied by various researchers in the context of point, area and barrier coverage problems. The sweep coverage problem on a graph is studied in [11]. In this problem, for a given input graph, minimum number of mobile sensors must be deployed such a way that all nodes of the graph must be visited by at least one mobile sensor. The sweep coverage problem is NP-hard and can not be approximated within a factor of 2 [16]. In this paper, we propose a fault-tolerant sweep coverage algorithm. For a given integer, using our proposed algorithm, the non-faulty sensors adjusted their movement and guarantees sweep coverage of all the nodes of an undirected weighted graph if at most sensors becomes faulty.
Many heuristic algorithms have been proposed in the literature to solve the team formation problem. The researchers considered a project as a set of skills selected randomly from the given pool of skills. But this leads to a skewed distribution of skills in the projects with many skills having very few experts, which we term as rare skills. In this work, we create a realistic bench-mark dataset for this problem. In general, any project/task in the industry can be seen to have a good mix of popular as well as rare skills. We first conduct an empirical study of the distribution of popular skills vs rare skills in the well-known DBLP (Digital Bibliography & Library Project) data set. The distribution of popularity of skills is shown to satisfy a power law with a heavy tail, indicating the presence of a large number of skills with very few experts and a small number of highly popular skills. We build a realistic a benchmark dataset using stratified random sampling to form tasks with various distributions of popular and rare skills. The classical team formation algorithms are evaluated using this new benchmark dataset. The evaluation is done with respect to the available communication costs in the literature as well as the execution time incurred by the algorithms. It has been observed from the experiments that all the measures show lower values of communication cost for tasks having higher proportion of popular skills.
Several professional societies have advocated for structured reporting in radiology, citing gains in quality, but some studies have shown that rigid templates and strict adherence may be too distracting and lead to incomplete reports. To gain the advantages of structured reporting while requiring minimal change to a radiologist's work-flow, the present work proposes a two-stage abstractive summarization approach that first finds the key findings in an unstructured report and then generates and organizes descriptions of each finding into a given template. The method uses a large manually annotated dataset and a taxonomy and other domain knowledge that were prepared in consultation with several practising radiologists. It can be used to structure reports dictated by radiologists and as post- and pre-processing steps for machine-learning pipelines. On the subtask of label extraction, the method achieves significantly better performance than previous rule-based approaches and learning-based approaches that were trained on automatically extracted labels. On the task of summarization, the method achieves more than 0.5 BLEU-4 score across 8 of the 10 most common labels and serves as a strong baseline for future experiments.
Flow table overflow attack on data plane devices is one of the prominent vulnerabilities in the Software Defined Networking (SDN) architecture. Flow table uses limited-sized TCAM to store the flow rules in the data plane. Unfortunately, TCAM based Flow tables are prone to various attacks such as memory saturation attacks, DDoS attacks, cross-plane attacks, Flow table overflow attacks, etc. These attacks lead to the starvation of benign requests, and saturation of network resources. However, the existing solutions are focused on the controller-based attack mitigation mechanism using OpenFlow switches which increases communication overhead between the control plane and data plane. This paper proposes a switch centric based in-network Flow table overflow attack detection and mitigation framework. We introduce IP_SourceGuard which keeps an audit of the flow rules by counting the threat value of a particular port. Mitigating the attack traffic when the threat value exceeds the limit of the warning threshold. Further, IP_SourceGuard blocks the attacker port from further not communicating it to the network. The solution has been implemented using the BMv2 software switch and determined that the solution reduces the Flow table utilization to 88%. From the result, it is observed that our solution mitigates the Flow table overflow attack in a real-time environment.
