StuCoSReC

Eleven papers addressed this conference, covering several topics of the computer science. All the papers were reviewed by two international reviewers and accepted for the oral presentation. This fact confirms a good work with authors in their research institutions. The content of the papers will be presented in three sections covering different areas of computer science and even robotics

Mobility Prediction Based Neighborhood Discovery for Mobile Ad Hoc Networks

Hello protocol is the basic technique for neighborhood discovery in wireless ad hoc networks. It requires nodes to claim their existence/aliveness by periodic `hello' messages. Central to any hello protocol is the determination of `hello' message transmission rate. No fixed optimal rate exists in the presence of node mobility. The rate should in fact adapt to it, high for high mobility and low for low mobility. In this paper, we propose a novel mobility prediction based hello protocol, named ARH ({\em Autoregressive Hello protocol}). In this protocol, each node predicts its own position by an ever-updated autoregression-based mobility model, and neighboring nodes predict its position by the same mobility model. The node transmits `hello' message (for location update) only when the predicted location is too different from the true location (causing topology distortion), triggering mobility model correction on both itself and each of its neighbors. ARH evolves along with network dynamics, and seamlessly tunes itself to the optimal configuration on the fly using local knowledge only. Through extensive simulation, we demonstrate the effectiveness and efficiency of ARH, in comparison with the best known competitive protocol TAP (Turnover based Adaptive hello Protocol). It comes out that ARH achieves the same high neighborhood discovery performance as TAP with dramatically less message overhead (about 50% lower `hello' rate)

Energy Efficient Uplink Transmission in Cooperative mmWave NOMA Networks with Wireless Power Transfer

In 5G wireless networks, cooperative non-orthogonal multiple access (NOMA) and wireless power transfer (WPT) are efficient ways to improve the spectral efficiency (SE) and energy efficiency (EE). In this paper, a new cooperative NOMA scheme with WPT is proposed, where EE optimization with a constrained maximum transmit power and minimum required SE is considered for the user grouping and transmit power allocation of users. We obtain a sub-optimal solution by decoupling the original problem in two sub-problems: an iterative algorithm is considered for the user grouping, while, in addition, we utilize the Bat Algorithm (BA) for solving the power allocation problem, where BA was proved to be able to achieve a higher accuracy and efficiency with respect to other meta-heuristic algorithms. Furthermore, to validate the performance of the proposed system, analytical expressions for the energy outage probability and outage probability of users are derived, confirming the effectiveness of the simulation results. It is demonstrated that the proposed cooperative NOMA with WPT offers a considerable improvement in terms of SE and EE of the network compared to other methods. Finally, the effectiveness of BA in solving the EE optimization problem is demonstrated through a high convergence speed by comparing it with other methods

SIMUBASE : using data base capabilities to implement interface free modules for building a simulation management environment

This is an attempt to deal with interface problems of interacting modules in a large simulation system. In simulation, scientists require routine substitution of modules for comparison of al­ternative models. These changes have propagating side affects and from a programmer's point of view are essentially system redesigns. This paper presents comparisons of several approaches attempting to resolve the above problem. Proposed here is an accessible design. The features of a Data Base Management System (DBMS) are used to implement a prototype (SIMUBASE) showing the feasibility of this approach. SIMUBASE is a simulation environment for manag­ing simulation modules.Index terms Modular Programming, Plugable Module, Reusable, Data-Item, Automatic Making, Standard Module Description, Version Selection Procedure, Module Master Name, Module Version Name, Simulation Script

Applied Randomized Algorithms for Efficient Genomic Analysis

The scope and scale of biological data continues to grow at an exponential clip, driven by advances in genetic sequencing, annotation and widespread adoption of surveillance efforts. For instance, the Sequence Read Archive (SRA) now contains more than 25 petabases of public data, while RefSeq, a collection of reference genomes, recently surpassed 100,000 complete genomes. In the process, it has outgrown the practical reach of many traditional algorithmic approaches in both time and space. Motivated by this extreme scale, this thesis details efficient methods for clustering and summarizing large collections of sequence data. While our primary area of interest is biological sequences, these approaches largely apply to sequence collections of any type, including natural language, software source code, and graph structured data. We applied recent advances in randomized algorithms to practical problems. We used MinHash and HyperLogLog, both examples of Locality- Sensitive Hashing, as well as coresets, which are approximate representations for finite sum problems, to build methods capable of scaling to billions of items. Ultimately, these are all derived from variations on sampling. We combined these advances with hardware-based optimizations and incorporated into free and open-source software libraries (sketch, frp, lib- simdsampling) and practical software tools built on these libraries (Dashing, Minicore, Dashing 2), empowering users to interact practically with colossal datasets on commodity hardware