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A sound water management requires accurate field data inquiry that can eventually allow for a proper control of the field’s irrigation. This work pro-poses an automatic irrigation control system that aims at optimizing the quantity of water needed for plants’ growth. The system consists of three key blocks: first, the continuous inquiry of the physical characteristics of the plant; second, the control unit to decide upon the actions to launch by the actuation unit; and third the actuation unit that sets parameters for pumps and valves. Conventional smart irrigation control systems use soil moister sensors which neglect the plant’s physiology as a monitoring factor, so this paper suggests the use of a sap flow sensor that accurately evaluates the plant’s condition; hence, decide upon the proper water distribution, illustrated with preliminary experimental results.

Nowadays, Genomic data constitutes one of the fastest growing datasets in the world. As of 2025, it is supposed to become the fourth largest source of Big Data, and thus mandating adequate high-performance computing (HPC) platform for processing. With the latest unprecedented and unpredictable mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the research community is in crucial need for ICT tools to process SARS-CoV-2 RNA data, e.g., by classifying it (i.e., clustering) and thus assisting in tracking virus mutations and predict future ones. In this paper, we are presenting an HPC-based SARS-CoV-2 RNAs clustering tool. We are adopting a data science approach, from data collection, through analysis, to visualization. In the analysis step, we present how our clustering approach leverages on HPC and the longest common subsequence (LCS) algorithm. The approach uses the Hadoop MapReduce programming paradigm and adapts the LCS algorithm in order to efficiently compute the length of the LCS for each pair of SARS-CoV-2 RNA sequences. The latter are extracted from the U.S. National Center for Biotechnology Information (NCBI) Virus repository. The computed LCS lengths are used to measure the dissimilarities between RNA sequences in order to work out existing clusters. In addition to that, we present a comparative study of the LCS algorithm performance based on variable workloads and different numbers of Hadoop worker nodes.

Predicting energy consumption in Smart Buildings (SB), and scheduling it, is crucial for deploying Energy-efficient Management Systems. Most important, this constitutes a key aspect in the promising Smart Grids technology, whereby loads need to be predicted and scheduled in real-time to cope for the strongly coupled variance between energy demand and cost. Several approaches and models have been adopted for energy consumption prediction and scheduling. In this paper, we investigated available models and opted for machine learning. Namely, we use Artificial Neural Networks (ANN) along with Genetic Algorithms. We deployed our models in a real-world SB testbed. We used CompactRIO for ANN implementation. The proposed models are trained and validated using real-world data collected from a PV installation along with SB electrical appliances. Though our model exhibited a modest prediction accuracy, which is due to the small size of the data set, we strongly recommend our model as a blue-print for researchers willing to deploy real-world SB testbeds and investigate machine learning as a promising venue for energy consumption prediction and scheduling.

Smart Grids (SG) have emerged as a remedy for the constantly increasing demand on energy. Control is and will continue to be the most crucial element in a SG. The integration of renewable energies and smart devices opened the door for many controllers to be used in order to help fulfill the objectives of SGs. These controllers are used to control and monitor the energy produced by the renewable sources, the energy consumed by the different loads and appliances, and the energy flow between the electrical grid, the storage module, and the produced energy. In this paper, we are presenting an architecture of a real-world SG featuring the NI CompactRIO controller. The SG is deployed in a university campus as a testbed. We are also describing a control algorithm based on a state machine deployed in the NI CompactRIO controller using the LabVIEW programing language.

Smart Grids (SG) are emerging as a very promising technology meant to cope with the stringent worldwide demand on energy and on relevant ecologic measures. At the heart of the Smart Grids lie Smart Buildings (SB). SB are the building blocks of SG. Either residential or industrial, SBs are consuming most of the produced electrical energy. However, and in the context of SG, SBs are meant to produce energy as well and contribute to stabilizing the Demand/Response (DR) variance whereby produced energy is injected back into the SG. This occurs mainly in case of energy shortage in the main SG or in case of excess in energy production. To leverage “smartness” in buildings, continuous data monitoring (e.g., energy production/consumption levels) using wireless sensors, and real-time dissemination/processing of this data, is essential. To further boost buildings’ smartness, deploying context-awareness, whereby electrical appliances can be switched On/Off depending on context (e.g., presence and ambient temperature), would bring considerable added-value. In this paper, a blueprint for deploying a real-world SB testbed is presented.

In the last couple of years, the use of renewable energies, mainly photovoltaic and wind, and their integration into Smart Grids (SG), has become more appealing. Renewable Energies reduce electrical energy production cost while promoting ecological measures. Micro-grids (MG), and distributed energy resources (DER), are gaining further momentum around the world as the main building blocks of SG. MG are meant to reduce the deployment and management complexity of SG by adopting a divide and conquer approach whereby SG are mainly reduced into a set of MGs. At the MG level, energy-efficiency can be further boosted via the minimization of energy loss (e.g., during power transmission) and via real-time tracking of energy production and consumption at the level of electrical appliances in order to optimize the Demand/Response (DR) variability. In this paper, we highlight issues related to real-world micro-grid deployment in a university campus. We mainly focus on profiling energy usage in campus buildings, setting power distribution system architecture, and pinpointing key micro-grid components. We propose a general MG testbed and simulate the operation of proposed MG model/architecture. We delineate relevant pros and cons towards a futuristic real-world/physical MG deployment in a university campus.

Software design is the most crucial step in the software development process that is why it must be given a good care. Software designers must go through many modeling steps to end up with a good design that will allow for a smooth development process later. For this, designers usually have to choose between two main modeling methodologies: Merise and UML. Both methodologies are widely used; however, each one has its own advantages and disadvantages. This paper combines both techniques and merges their advantages to come up with an approach that would help software designers make the best of both methodologies. This integration mainly targets the software design step in general but can be specifically applied to database design. It presents the weaknesses and strengths of each one of UML and Merise as two techniques used in database modeling and design. Later in this paper, a comparing of UML and Merise diagrams is lead and based on it, the decision on which of the two is the best at each step of the modeling process.