Abstract
This paper presents a PhD thesis proposal in Informatics Engineering, scheduled for completion in July 2018. This PhD thesis is part of Spatio-Temporal Information Systems, with applicability in technological communication tools and visual representation of knowledge, for Digital Media (newspapers, radio and television). It is intended to maximize the efficiency and effectiveness of the value of heterogeneous, multivariate, multidimensional information characteristic of this context, produced and shared by different sources, in different formats. It is hoped that participation in this Doctoral Symposium will enrich and update the work in progress and help the preparation of the PhD thesis proposal.

Abstract
Here we propose a simplified model for the path planning of an Autonomous Underwater Vehicle (AUV) in an horizontal plane when ocean currents are considered. The model includes kinematic equations and a simple dynamic equation. Our problem of interest is a minimum time problem with state constraints where the control appears linearly. This problem is solved numerically using the direct method. We extract various tests from the Maximum Principle that are then used to validate the numerical solution. In contrast to many other literature we apply the Maximum Principle as defined in [9].

Abstract

Abstract
With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, plug-in electric vehicle (PEV) and demand response have indispensable impacts on the future smart grid. Since integration of PEVs into the grid is a key element to achieve sustainable energy systems, this paper presents the optimal behavior of PEV parking lots in the energy and reserve markets. To this end, a model is developed to derive optimal strategies of parking lots, as responsive demands, in both price-based and incentive-based demand response programs (DRPs). The proposed model reflects the impacts of different DRPs on the operational behavior of parking lots and optimizes the participation level of parking lots in each DRP. Uncertainties of PEVs and electricity market are also considered by using a stochastic programming approach. Numerical studies indicate that the PEV parking lots can benefit from the selective participation in DRPs.

Abstract
Interference is a fundamental issue in wireless mesh networks (WMNs) and it seriously affects the network performance. In this paper we characterize the power interference in IEEE 802.11 CSMA/CA based wireless mesh networks using directional antennas. A model based centralized call admission control (CAC) scheme is proposed which uses physical collision constraints, and transmitter-side, receiver-side and when-idle protocol collision prevention constraints. The CAC assists to manage requests from users depending on the available bandwidth in the network: when a new virtual link establishment request from a user is accepted into the network, resources such as interface, bandwidth, transmission power and channel are allocated in the participating nodes and released once the session is completed. The proposed CAC is also able to contain the interference in the WMN by managing the transmission power of nodes.

Abstract
A traffic tensor or simply origin x destination x time is a new data model for conventional origin/destination (O/D) matrices. Tensor models are traffic data analysis techniques which use this new data model to improve performance. Tensors outperform other models because both temporal and spatial fluctuations of traffic patterns are simultaneously taken into account, obtaining results that follow a more natural pattern. Three major types of fluctuations can occur in traffic tensors: mutations to the overall traffic flows, alterations to the network topology and chaotic behaviors. How can we detect events in a system that is faced with all types of fluctuations during its life cycle? Our initial studies reveal that the current design of tensor models face some difficulties in dealing with such a realistic scenario. We propose a new hybrid tensor model called HTM that enhances the detection ability of tensor models by using a parallel tracking technique on the traffic's topology. However, tensor decomposition techniques such as Tucker, a key step for tensor models, require a complicated parameter that not only is difficult to choose but also affects the model's quality. We address this problem examining a recent technique called adjustable core size Tucker decomposition (ACS-Tucker). Experiments on simulated and real-world data sets from different domains versus several techniques indicate that the proposed model is effective and robust, therefore it constitutes a viable alternative for analysis of the traffic tensors.