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Recently, Deep Neural Networks have shown great deal of reliability and applicability as its applications spread in different areas. This paper proposes a cryptanalysis model based on Deep Neural Network, the neural network takes in plaintexts and their corresponding ciphertexts to predict the secret key of the cipher. We proposes two different approaches, in the first we use multi-layer perceptron (MLP). While in the second, the cryptanalysis problem is modeled as a multi-label classification problem, we introduce appropriate Deep Neural Network based methods for tackling such problem. We illustrate the effectiveness of the approach of the DNN-based cryptanalysis by attacking on Simplified AES block cipher. Therefore, specific metrics are readapted to the cryptanalysis context and used to evaluate the proposed schemes. The results indicate that treating cryptanalysis problem as multi-label classification is more suitable and can be a useful and promising tool for the cryptanalysis task.

In this paper, we are interested to forecast and predict the time evolution of the Covid-19 in Morocco based on two different time series forecasting models. We used Auto-Regressive Integrated Moving Average (ARIMA) and Long short-term memory (LSTM) models to predict the outbreak of Covid-19 in the upcoming 2 months in Morocco. In this work, we measured the effective reproduction number using the real data and also the fitted forecasted data produced by the two used approaches, to reveal how effective the measures taken by the Moroccan government have been controlling the Covid-19 outbreak. The prediction results for the next 2 months show a strong evolution in the number of confirmed and death cases in Morocco. According to the measures of the effective reproduction number, the transmissibility of the disease will continue to expand in the next 2 months, but fortunately, the higher value of the effective reproduction number is not considered to be dramatic and, therefore, may give hope for controlling the disease.

The demand for electricity is increased due to the development of the industry, the electrification of transport, the rise of household demand, and the increase in demand for digitally connected devices and air conditioning systems. For that, solutions and actions should be developed for greater consumers of electricity. For instance, MG (Micro-grid) buildings are one of the main consumers of electricity, and if they are correctly constructed, controlled, and operated, a significant energy saving can be attained. As a solution, hybrid RES (renewable energy source) systems are proposed, offering the possibility for simple consumers to be producers of electricity. This hybrid system contains different renewable generators connected to energy storage systems, making it possible to locally produce a part of energy in order to minimize the consumption from the utility grid. This work gives a concise state-of-the-art overview of the main control approaches for energy management in MG systems. Principally, this study is carried out in order to define the suitable control approach for MGs for energy management in buildings. A classification of approaches is also given in order to shed more light on the need for predictive control for energy management in MGs.

Increases in power demand and consumption are very noticeable. This increase presents a number of challenges to the traditional grid systems. Thus, there is the need to come up with a new solution that copes with the stringent demand on energy and provides better power quality, which gives a better experience to the end users. This is how the concept of smart grids (SG) came to light. SGs have been introduced to better monitor and control the power produced and consumed. In addition to this, SGs help with reducing the electricity bill through the integration of renewable energy sources. The underlying smartness of the SGs resides in the flow of information in addition to the flow of energy. Information/data flowing implies the use of smart sensors and smart meters that sense and send data about the power produced and consumed, and the data about the environment where they are deployed. This makes SGs a direct application of IoT. In this paper, we are implementing an edge platform that is based on single-board computers (SBCs) to process data stemming from SG. The use of SBCs is driven by the energy efficiency and cost effectiveness concepts that the SG is trying to apply. The platform in question is tested against a distributed job that averages random numbers using Hadoop’s MapReduce programming model. The SBC that we are using in this implementation is the NVIDIA Jetson Developer Kit. The results of this work show that a cluster of SBCs is low-cost, easy to maintain, and simple to deploy, which makes it a great candidate for providing edge computing. Although it revealed a performance that beat the one of the remote cloud servers, it could not outperform the single-computer edge platform.

The major objectives of this work are: 1) to develop new efficient optimization algorithm to solve NP-hard problems, 2) to show the potential of integrating renewable energy technologies for Laayoune region- Morocco taken as case study. In this work, new parallel hybrid Genetic Algorithm-Particle Swarm Optimization algorithm (P-GA-PSO) is developed to solve both sizing and energy management problems for micro-grids. The studied micro-grid is composed of four different renewable energy technologies and energy storage system. The objectives of both optimization problems are to satisfy typical load demand, to minimize energy production cost, to maximize renewable energy integration, to avoid energy losses and overload. P-GA-PSO performances are evaluated and compared with the ordinary optimization methods based on: 1) a set of benchmark functions, 2) various scenarios used to solve the proposed micro-grid optimization problems. Obtained results demonstrated that P-GA-PSO outperforms ordinary optimization algorithms in terms of convergence time and solution quality. Moreover, the proposed micro-grid is very promising in terms of load demand satisfaction, cost and pollutant emissions reduction. Indeed, the obtained energy cost does not exceed 0.17 US$/kWh, which is close to fossil fuel energy cost, and fossil fuel replacement rate exceeds 50% during all periods.

Micro-grid systems have been recently emerged for efficient integration and management of renewable energy sources, buildings’ equipment (e.g. ventilation; lighting; heating, ventilation, and air conditioning), and energy storage devices. The intermittent nature of the produced electricity from renewable energy sources constitutes, however, the main challenge for their seamless integration into buildings. Recent studies stated that storage devices, despite their actual high cost, could be used to tackle this issue by balancing between the variability of renewable energy sources production and the unpredictable building’s occupancy. However, managing the power flows in micro-grid systems is highly required in order to minimize the consumption from the electric grid while ensuring the quality and the reliability of electrical services. In this article, a micro-grid system platform is introduced for efficient integration and management of renewable energy sources and storage devices. The platform’s architecture is composed of three main components: (a) the power generation components that integrate the renewable energy sources together with the storage devices and the traditional electrical grid, (b) an advanced metering component for data gathering and analysis, and (c) control services for power flow management. The platform was developed and deployed in a real-setting scenario. Simulations and experimentations have been performed and results show the usefulness of the proposed platform for efficient management of the deployed micro-grid system.

In this work, a new hybrid cuckoo search and genetic algorithm optimization method using a novel adaptive penalty function was proposed to solve the economic dispatch (ED) problem in smart grid. Please check and confirm the edit made in article title. This method was also paralyzed in order to solve the problem within specific time suitable to solve Energy management Problems. Three improvements are achieved through this combination. First, parallelism allows further reduction of the execution time. Second, the hybridization of both cuckoo search and genetic algorithm methods allows better diversification and exploration of search space which increases the solution quality. Third, the new adaptive penalty function was developed to discard infeasible solutions and to choose near-optimal ones within a short time. The efficiency of the developed algorithm is proven theoretically and experimentally. Three scenarios are considered to prove experimentally the out-performance of the developed method: (1) the proposed method is compared with Cuckoo Search and Genetic Algorithm methods using a set of benchmark functions. (2) A comparative study is carried out by applying the method to the ED continuous problem optimization case study. (3) The method is compared with Cuckoo search to solve discrete demand side management problem, considering each consumer as an independent parameter. The performance evaluation was conducted using Matlab data parallelism library.

The Merkle-Hellman (MH) cryptosystem is one of the earliest public key cryptosystems, which is introduced by Ralph Merkle and Martin Hellman in 1978 based on an NP-hard problem, known as the subset-sum problem. Furthermore, ant colony optimization (ACO) is one of the most nature-inspired meta-heuristic optimization, which simulates the social behaviour of ant colonies. ACO has demonstrated excellent performance in solving a wide variety of complex problems. In this paper, we present a novel ant colony optimization (ACO) based attack for cryptanalysis of MH cipher algorithm, where two different search techniques are used. Moreover, experimental study is included, showing the effectiveness of the proposed attacking scheme. The results show that ACO based attack is more suitable than many other algorithms like genetic algorithm (GA) and particle swarm optimization (PSO).

The resent infrastructure of the buildings is becoming socio-technical systems that integrate different heterogeneous entities (e.g., sensors, actuators, lighting, HVAC (Heating, Ventilation, and Air Conditioning), occupants, renewable energy, storage systems), which could interact dynamically and in a collective manner to balance between energy efficiency, occupants’ comfort, sustainability, and adaptability. This new infrastructure is known as the “Micro-grid” (MG) system concept. However, the main challenge for this infrastructure is real-time monitoring and data processing, which requires the use of new information and communication technologies. In addition, incorporating mechanisms and techniques are required in order to have buildings more energy-efficient while ensuring occupants’ comfort by allowing entities interaction for suitable actions (e.g., turning On/Off HVAC and lighting, balancing the fluctuation between power production and consumption). In this work, a new holistic architecture of smart buildings is presented by improving the main layers of MG systems. This architecture is proposed in order to integrate all buildings’ aspects with the main trade-off is to efficiently manage the building while maintaining a suitable occupants’ comfort. In fact, an MG system is structured into three layers following the proposed holistic architecture. More precisely, we shed more light on the MG system’s layer by integrating recent IoT/Big-Data technologies for data gathering, processing, and control. A set of sensors are installed for power measurement in the MG system while the measured data is gathered and transmitted to an IoT/Big-Data platform for analysis, processing, and storing. The main aim is to store a historical of data (e.g., power production/consumption, weather conditions) from the installed MG system with open-access tools.

Recent studies showed that energy consumption in buildings could be efficiently reduced by including recent IoT (Internet of Things) and Big-Data technologies into microgrid systems. In fact, three major aspects could be further considered for reducing energy consumption while maintaining a suitable occupants’ comfort, (i) integrating renewable energy sources and storage devices, (ii) integrating programmable and less-energy-consuming equipment, and (iii) deploying innovative information and communication technologies. These aspects might contribute substantially to the improvement of winning and saving energy toward smart and energy-efficient buildings. In this chapter, a microgrid system infrastructure is developed together with a platform for data gathering, monitoring, and processing. We put more emphasis on microgrid systems as crucial infrastructures for leveraging energy-efficient and smart buildings by developing and deploying a holistic IoT/Big-Data platform in which sensing and actuation tasks are performed according to the actual contextual changes. Scenarios are presented in order to show the usefulness of this holistic platform for monitoring, data processing, and control in energy-efficient buildings.

The accurate position of nodes in Wireless Sensor Networks (WSNs) is considered a critical problem in the majority of the Internet of Things (IoT) applications. Recently a large number of contribution in localization have been recommended to determine the location of nodes. However, a number determinated of these techniques have been presented to precisely determine the nodes locations in the IoT. In this work, we discuss the new three localization techniques, named Centroid + 4A, ICentroid, and ICentroid + 4A respectively, based on the Centroid localization technique and a new weighted formula to estimate the target nodes’ positions. The OMNeT++ network simulator was used to figure out and the performance of the discussed solutions in comparison with the Centroid localization technique. The examined results reveal that a significant improvement in the localization precision of the discussed contributions in Wireless Sensor Networks.

The intermittent nature of renewable energy sources has attracted much interest for integrating storage devices towards the deployment of micro-grid systems. In fact, these devices could be deployed into the system by decoupling energy production from consumption mainly by storing the extra produced energy from RES during the day for eventual usage at night. However, for standalone systems, computing the optimum size of energy production systems and storage devices is required for continuous electricity supply. In this paper, a micro-grid system is modeled, sized, and experimented according to a given consumption profile using a deployed prototype at our EEBLab.

Information broadcasting in wireless network is a necessary building block for cooperative operations. However, the broadcasting causes increases the routing overhead. This paper brings together an array of tools of our adaptive protocol for information broadcasting in MANETs. The proposed protocol in this paper named WAODV (WAIT-AODV). This new adaptive routing discovery protocol for MANETs, lets in nodes to pick out a fantastic motion: both to retransmit receiving request route request (RREQ) messages, to discard, or to wait earlier than making any decision, which dynamically confgures the routing discovery feature to decide a gorgeous motion through the usage of neighbors’ knowledge. Simulations have been conducted to show the effectiveness of the using of techniques adaptive protocol for information broadcasting RREQ packet when integrated into ad hoc on-demand distance vector (AODV) routing protocols for MANET (which is based on simple flooding).

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.

The integration of Combined Heat and Power (CHP) systems, especially fuel cell systems, into buildings have attracted a lot of interest these last years. It is due to their higher efficiency, cleanliness and their capability for producing both valuable thermal energy and electricity, from a single source of fuel, such as natural gas or hydrogen. Their high efficiency, which can reach 85%–90%, leads to a decrease in both greenhouse gas emissions and costs when compared to the conventional methods of generating heat and electricity separately. The aim of this work is towards efficient integration of a Proton Exchange Membrane (PEM) fuel cell into buildings in order to supply it with both electricity and heat. Experiments on a system composed of fuel cell, converter, and hydrogen tank have been carried out and compared to the simulations conducted using Matlab/Simulink where the system’s model has been developed. Simulation and experimental results are compared in order to show the accuracy of the models that have been developed and the efficiency of the fuel cell when operating as a CHP system to supply the building with both electricity and heat. Keywords: CHP system, Fuel cell, Hydrogen, Matlab/Simulink softwares

Green Computing has been the trend among computer scientists for its eco-friendliness. It serves as a great solution to be integrated with Smart Grids (SG). Data stemming from SGs falls under the realm of Big Data as it is voluminous, various, and has a great velocity. Hence, these data need processing and storage. For this, High-Performance Computing, through clustering a set of computers, proves necessary. Nowadays, with the hardware advances that the world is witnessing, the Raspberry Pi (RP) creates a number of opportunities to deploy cost-effective and energy-efficient clusters, which respect the concepts of Green Computing. In this paper, we are presenting the work done within a USAID sponsored project which aims at developing a SG testbed at Al Akhawayn University in Ifrane, Morocco. We are presenting the deployment of a 5-node cluster based on RPs. The cluster has Hadoop installed and runs the TestDFSIO and Terasort benchmarks for the performance analysis in addition to an energy efficiency analysis.

New sources of fuel have been extensively searched in order to replace fossil products. Among these sources, hydrogen has attracted much interest due to its cleanliness, availability, and efficiency. It can be produced from numerous resources, such as water electrolysis, natural gas reforming. For instance, it is widely used by fuel cells in vehicles and buildings in order to produce both valuable electricity and thermal energy. The electrolysis of water for producing hydrogen using renewable energy sources, especially solar photovoltaic systems, is one of the appropriate solutions to produce hydrogen. This paper focuses on the performance analysis of a system consisting of a grid connected photovoltaic panels, an electrolyzer for producing hydrogen, and a proton exchange membrane fuel cell operating as a cogeneration system for supplying the building with both electricity and thermal energy. The particularity of this system is that the production of the electricity using photovoltaic panels and the production of hydrogen using electrolyzer can be performed in different sites and are connected through the traditional electricity grid. As a result, the overall efficiency of the studied system reaches 60%, which shows its effectiveness when supplying the building with both electricity and thermal energy.

Mobile Ad Hoc Networks are wireless networks, of which the installation does not require any existing infrastructure, this property has induced the philosophy of “all distributed” and distributed control. The departure and arrival of a node is autonomous and does not interfere with the continuity of services. These advantages allow the deployability of these networks in situations where the use of wired networks is expensive, difficult or even impossible. However, with these advantages, some security concerns are emerging. Indeed, with a shared communication medium and in the absence of a central entity or a fixed infrastructure, traditional security solutions are not adapted, it is now difficult to manage encryption keys, certificate distribution, and trust management between nodes. As a node does not necessarily have knowledge about others when the network is growing, trust a priori may not exist. We are interested in this paper to study security problems in Manets in general, and more particularly those related to trust management. This paper detail, the principle, the constraints, and the challenges of deploying suitable trust models, to deal with Manets constraints. An example of a light trust model for enforcing security in OLSR routing protocol used for Manets, is presented and evaluated.

Balancing production and demand of energy is the main challenge to integrate renewable energy sources (RES) in micro-grid (MG) systems. However, the variability and the uncertainty nature of the production and the consumption make the system more difficult to control. In fact, weather conditions influence on the production of renewable energy sources while occupancy influences on the power consumption. Therefore, the development of accurate short term forecasts are needed for a seamless integration of RES (e.g. photovoltaic system, wind turbine) together with the traditional electrical grid in MG systems. This paper presents a forecasting model for predicting the power production and consumption in MG systems together with the battery state of charge (SoC). A control strategy is then implemented to balance the Demand/Response by taking into account the forecasted and real-time values. Based on the data collected from a real MG system, simulation results are presented to show the effectiveness of power forecasting for MG control.

Ant colony Optimization is a nature-inspired meta-heuristic optimization algorithm that gained a great interest in resolution of combinatorial and numerical optimization problems in many science and engineering domains. The aim of this work was to investigate the use of Ant Colony Optimization in cryptanalysis of Simplified Advanced Encryption Standard (S-AES), using a known plaintext attack. We have defined the essential components of our algorithm such as heuristic value, fitness function and the strategy to update pheromone trails. It is shown from the experimental results that our proposed algorithm allow us to break S-AES cryptosystem after exploring a minimum search space when compared with others techniques and requiring only two plaintext-ciphertext pairs.

Localization phase for sensor nodes is considered as the most important service for many applications of IoT. In the last decides, many localization algorithm have been presented to provide location of sensor nodes, but a limited number of these algorithms have been introduced to accurately estimate the location of sensor nodes in the Internet of Things (IoT). In this paper, we propose localization algorithms for sensor nodes in WSNs. The algorithms based on Centroid localization algorithm for Internet of Things. The Proposed algorithm is based on the new weighted formula to calculate the position of unknown nodes. Our presented algorithms guarantee that the nodes estimate their own position with a butter localization accuracy in comparison to the Centroid algorithm and DV-Hop in all considered scenarios. Moreover, the proposed simulation confirms that the introduced algorithms can significantly enhance the localization error and of algorithms in Wireless Sensor Networks.

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.

In most of Wireless Sensor Network (WSN) applications, it is important to associate each event’s information with its location’s information. If sensors are accurately located, the identified event (e.g., fire, accident) could be of most important in order to figure out and understand the context or situation. However, sensors localization is still an important issue in order to develop context-driven applications. This paper introduces a new scheme for sensor nodes localization in three-dimensional WSNs, denoted 3DeDV-Hop. We have implemented both algorithms the 3DeDV-Hop and 3DDV-Hop in OMNET++, and studied their performance in three dimensional wireless sensor networks. Simulation results show that the 3DeDV-Hop outperforms the basic 3DDV-Hop localization algorithm in terms of localization accuracy and the number of localized nodes.

The integration of renewable energy sources (RES) was amplified, during the past decades, in order to tackle the challenges related to energy demands and CO2 increases. Recently, many initiatives have been taken by promoting the deployment and the usage of micro-grids (MG) in buildings, as decentralized systems, for energy production. However, the variable nature of RESs and the limited size of energy storage systems require the deployment of adaptive control strategies for efficient energy balance. In this paper, a generalized predictive control (GPC) strategy is introduced for energy management (EM) in MG systems. Its main objective is to efficiently connect the electricity generators and consumers in order to predict the most suitable actions for energy flow management. In fact, based on energy production and consumption profiles as well as the availability of energy storage systems, the proposed EM will be able to select the best suitable energy source for supplying the building’s loads. It will efficiently manage the usage of energy storage and the utility grid while maximizing RESs power generation. Simulations have been conducted, using real-sitting scenarios, and results are presented to validate the proposed predictive control approach by showing its effectiveness for MG systems control.

Batteries have shown great potential for being integrated in Micro-Grid (MG) systems. However, their integration is not a trivial task and, therefore, requires extensive modeling and simulations in order to efficiently estimating their State-of-Charge (SoC) within MG systems. The work presented in this article focuses mainly on battery modeling for SoC estimation. We put more emphasize on battery system's characterization, modeling, SoC estimation and its integration into MG systems in order to study its performance. In fact, an instrumentation platform, composed of recent and low cost sensing and actuating equipment, is developed in order to identify the battery's parameters and then build the battery model. The simulation results show good agreement with the experimental results, and thus the battery model is validated in both charging and discharging processes. Then, the battery's SoC estimation in both charging and discharging processes is investigated by means of sophisticated methods, such as, Artificial Neural Network, Luenberger observer and Kalman Filter combined with Coulomb counting. The results of these methods are reported and compared to the SoC estimated using the Coulomb counting method in order to end up with the precise method. The modeled battery is afterwards integrated into an MG system, which is deployed into our EEBLab (Energy Efficient Building Laboratory) test-site, for simulation and experimental investigations. Finally, the four algorithms for SoC estimation are included into the instrumentation platform, which is connected to a Lead-acid battery already integrated into the MG system, in order to show and compare their performances and accuracy for online battery's SoC estimation.

The search for new fuels to supersede fossil fuels has been intensified these recent decades. Among these fuels, hydrogen has attracted much interest due to its advantages, mainly cleanliness and availability. It can be produced from various raw materials (e.g., water, biomass) using many resources, mainly water electrolysis and natural gas reforming. However, water electrolysis combined with renewable energy sources is the cleanest way to produce hydrogen while reducing greenhouse gases. Besides, hydrogen can be used by fuel cells for producing both electrical and thermal energy. The aim of this work was towards efficient integration of this system into energy efficient buildings. The system is comprised of a photovoltaic system, hydrogen electrolyzer, and proton exchange membrane fuel cell operating as a cogeneration system to provide the building with both electricity and thermal energy. The system&rsquo, s modeling, simulations, and experimentations were first conducted over a short-run period to assess the system&rsquo, s performance. Reported results show the models&rsquo, accuracy in analyzing the system&rsquo, s performance. We then used the developed models for long-run testing of the hybrid system. Accordingly, the system&rsquo, s electrical efficiency was almost 32%. Its overall efficiency reached 64.5% when taking into account both produced electricity and thermal energy.

M G systems integrate renewable energy sources (RES) together with storage devices for being connected to the traditional electrical grid in order to supply the power to the building's loads. However, management and control approaches are required for seamless integration of these systems in energy efficient buildings. In this paper, a model predictive control approach is introduced for efficient MG operation. The proposed approach allows minimizing the usage of the traditional electric grid for supplying the power to the building's loads by using as much as possible the power generated by RES while optimizing the storage devices operations. A model predictive control (MPC) strategy is proposed in order to balance the power flow in MG system. The deployed strategy controls the charge/discharge current of the battery depending on RES production, and load consumption variability. Simulations have been conducted using real dataset generated from our deployed MG system, and preliminary results show the usefulness of predictive control principles for the efficient operation of MG systems.

Ant Colony Optimization is a search meta-heuristic inspired by the foraging behavior of real ant, having a very wide applicability. Especially, it can be applied to different combinatorial optimization problem. In this paper, we present a novel Ant Colony Optimization (ACO) based attack for cryptanalysis of Simplified Data Standard Encryption (S-DES). A known Plaintext attack is used to recover the secret key requiring only two Plaintext-Ciphertext pairs. Moreover, our approach allows us to break S-DES encryption system in a minimum search space when compared with other techniques. Experimental results prove that ACO can be considered as a convincing tool to attack the key used in S-DES.

This work presents methodology and results used to estimate both wind and solar energies availability and their cost. The objective is to analyze the potential of these two major sources of alternative energy in Tangier region and to show their ability to replace fossil fuel energy sources especially for remote areas and agriculture applications. Thus, energy availability and its cost for these technologies are determined based on meteorological data, and their engineering and technical characteristics. The obtained results provide the hourly average energy production (in kWh) and its cost (in USD/kWh) for a maximum designed power output of 5 kW for each technology and for each month in the year. Thus, these results show that energy production cost is ranging between 0.01 and 0.3 USD/kWh for solar energy using photovoltaic panels and it is ranging between 0.05 and 0.35 USD/kWh for wind energy using wind turbines with rated power equals to 1 kW. Results of this work could be used as data estimation reference for engineering and research works related to renewable energy applications in the studied region.

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.

Renewable energy sources (RES) have been promoted to overcome environmental constraints as well as the scarcity of fossil energy sources. In fact, many initiatives have been deployed by integrating these sources (e.g. Photovoltaic panel, Wind, Fuel cell...) in order to increase energy efficiency (i.e., electricity production) in buildings while limiting CO 2 emissions. In parallel to this development, micro-grids have emerged as decentralized systems to allow connecting different decentralized sources in which buildings become active electricity producers in addition to the main traditional electric grid. However, a smarter demand response management system is required to efficiently balancing the energy production, storage, and the electricity consumption. In this paper, a demand/response control approach is introduced by integrating into a holistic platform different RES together with storage devices for supplying electricity to buildings equipment (e.g. ventilation system, lighting…). A prototype was developed and deployed in our EEBLab for experiments and real-testing. Preliminary experimental results are reported and show the usefulness of the proposed holistic approach.

Localization phase for sensor nodes is considered as a critical issue for Static and Mobile Wireless Sensor Networks (SWSNs & MWSNs) for Three-Dimensional (3D) in last decides. Several localization techniques have been introduced to provide position of sensor nodes, but a limited number of these algorithms have been proposed to accurately estimate the location of sensor nodes for mobile wireless sensor networks for three-dimensional. In this paper, we propose localization algorithms for mobile nodes in 3D. The algorithms based on DV-Hop and EDV-Hop techniques. The selected model is Random Way Point Mobility model. Our presented algorithms guarantees that the nodes estimate their own position with a butter localization accuracy in comparison to static scenarios. Moreover, the proposed simulation confirms that the introduced algorithms can significantly improve the localization error and localization coverage of algorithms in Mobile Wireless Sensor Networks for nodes in a Three- Dimensional.

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.

Geographic routing protocols have been developed to increase the effectiveness and the usefulness of wireless sensor networks in many emerging applications. In these protocols, the routing decision, at each sensor node, is based only on the positions of the destination node and forwarding node's neighbors. However, these protocols are based on exact sensors position, assuming that sensors are equipped with a geographic positioning or a localization-based technique. Recently, several techniques, such DV-Hop, have been proposed for estimating sensors' positions. This paper introduces an integrated geographic routing protocol by integrating both sensors' position estimation and geographic routing techniques. We have mainly combined DV-Hop for estimating sensors position and the greedy perimeter stateless protocol for data forwarding and routing. Simulations have been carried out and results are reported to show the efficiency of the proposed hybrid geographic routing protocol in WSNs.

Ant Colony Optimization is a search meta-heuristic inspired by the behavior of real ant colonies and shown their effectiveness, robustness to solve a wide variety of complex problems. In this paper, we present a novel Ant Colony Optimization (ACO) based attack for cryptanalysis of Linear Feedback Shift Registers (LFSR). A known plaintext attack is used to discover the primitive polynomial used in the LFSR. Moreover, our approach allows us to find a linear equivalence of a given key stream through finding the feedback function. Experimental results prove that ACO can be used as an effective tool to break LFSR.

The intermittent nature and overwhelming variability of the produced electricity from renewable energy sources (RES) constitutes the main challenge for their seamless integration into buildings. Recent studies stated that storage devises, despite their actual high cost, could be used to tackle this issue by balancing the intermittent nature of RES production and the unpredictable building's occupancy. In fact, these storage devises could handle this issue, which is very complex without taking into consideration actual occupancy and environmental context. This paper introduces a demand/response control approach by mainly focusing on micro-grid systems, which are composed of PVs, batteries, and buildings equipment (e.g., ventilation system, lighting, BVAC). A prototype was developed and deployed in a real-setting scenario at our EEBLAB. Experimentations have been performed and results show the usefulness of the proposed control approach for micro-grid systems.

Ant Colony Optimization is a search metaheuristic inspired by the behavior of real ant colonies and shown their effectiveness, robustness to solve a wide variety of complex problems. In this paper, we present a novel Ant Colony Optimization (ACO) based attack for cryptanalysis of knapsack cipher algorithm. A Cipher-text only attack is used to discover the plaintext from the cipher-text. Moreover, our approach allows us to break knapsack cryptosystem in a minimum search space when compared with other techniques. Experimental results prove that ACO can be used as an effective tool to attack knapsack cipher.

Renewable energy sources (RES) have been largely deployed as green energy producers in order to reduce power consumption while minimizing greenhouse emissions. However, the intermittent nature of RES (e.g., Solar Energy) must be addressed in order to allow their large scale integration in buildings. Standalone PV systems are generally installed in remote and rural regions since they are not connected to the electrical grid. They use mainly storage devices (e.g., batteries) to store produced/extra solar energy during the day for eventual usage at night. Recently, there have been enormous deployments of grid-connected photovoltaic systems, which use solar panels connected to the electricity grid for producing and feeding extra electricity back to the grid. They are also being able to supply electricity to the load when there is no production. In this paper, we are talking the research question aiming at selecting the suitable dimensions for energy production (PV system). We have, therefore, developed a model for each component of the PV system. A grid-connected PV system is sized, modeled and analyzed according to a dedicated consumption profile. Simulation results show the effectiveness of the proposed dimensioning model.

The solar or the Photovoltaic (PV) systems have been developed in the past few years as renewable energy sources in order to reduce power consumption while minimizing greenhouse emissions. However, getting the maximum power output in every type of environmental and climatic circumstances is of most importance and requires efficient modeling and extensive experimental studies. In this work, we present a modeling and experimental study of photovoltaic systems. The developed model uses weather conditions in order to produce the current and the voltage that are required to compute the produced power. Simulations have been conducted to first study the effect of irradiance and temperature on the PVs production. A testbed system was deployed to perform experiments in order to study the effectiveness and the accuracy of the proposed modeling approach in real-setting scenarios.

Context-awareness is crucial for leveraging energy-efficient buildings by developing intelligent control approaches in which sensing and actuation tasks are performed according to the contextual changes. This could be done by including the users' actions and behaviours in up-to-date context taking into account the complex interlinked elements, situations, processes, and their dynamics. In this paper, we introduce a holistic platform that integrates recent sensing/actuating and Big data technologies for monitoring and data processing. The main aim is to develop context-driven control approaches whereby energy consumption, production, and storage could be controlled according to actual situations (e.g., occupancy, occupant behaviour patterns, energy production patterns, and weather data). A platform prototype was deployed in our university test site. Experiments have been conducted and preliminary results show the usefulness of this holistic platform for monitoring and data processing in energy efficient buildings.

In this paper, we present a novel Ant Colony Optimization (ACO) based attack for cryptanalysis of a simple substitution Cipher. A known only cipher text attack is used to recovering the key based on the letter frequency language, a new algorithm is introduced with a Fitness Function that have a good correlation with the number of key elements recovered. Our approach shows promising results when compared with other techniques.

Micro-Grid (MG) systems have been extensively studied and deployed to lower the power consumption while reducing the greenhouse gas emissions. Although, the challenge with Renewable Energy Sources (RES) is their uncertain and intermittent nature, things that led the researchers to think about integrating storage devices, especially batteries, into MG systems. The main aim is to store the excess of produced energy for further usage when not enough energy is available. Nevertheless, batteries modeling and characterization is mandatory to identify their parameters and study their performance within MG systems. Moreover, in order to continuously supply electricity to the building, it is required to figure out the optimum size of energy production systems and storage devices. This paper introduces a methodology for MG modeling and performance evaluation. Its main contribution is twofold, i) battery’s parameters identification, and ii) modeling and dimensioning method for both standalone and MG systems. An instrumentation platform, composed of recent sensing and actuating equipment, for MG energy management and battery characterization is developed. Simulation and experimental results show the effectiveness of the proposed methodology.

Geographic protocols have been recently developed for data routing in wireless ad hoc networks. These protocols are based on the exact location of each sensor node, by assuming that sensor nodes are equipped with geographic positioning services or localization-based techniques. At each node, the routing decision is based only on the position of the destination node and the position of the forwarding node’s neighbors. Location-based techniques, such DV-Hop, have attracted a lot of interest in order to estimate nodes positions. In this paper, we evaluate the performance of DV-Hop localization technique for estimating nodes localization and integrate it within the greedy perimeter stateless routing protocol. Simulations have been conducted to show the effectiveness of using localization-based techniques when integrated into geographical routing protocols for wireless sensor networks.

Wireless nano sensors networks (WNSN) have emerged recently to allow nanonodes to communicate to each other using nano-communication mechanisms. Nanodevices are characterized by limited resources, and new communication protocols are, therefore, required. For instance, the flooding mechanism has been proposed for broadcasting in electromagnetic-based WNSNs. While the flooding scheme is very simple, the excessive number of rebroadcasts increases the utilization of the resources that affects the performance and increases the energy consumption. In this paper, we adopt the probabilistic-based broadcasting mechanism in the context of WNSNs. Simulations have been conducted using Nanosim and results show that probabilistic-based flooding mechanism reduces redundant retransmissions while maintaining good performance.

The Numeral features extraction consists of transforming the image into an attribute vector, which contains a set of discriminated characteristics for recognition, and also reducing the amount of information supplied to the system. Several characteristic extractions methods have been proposed in the literature. These characteristics can be digital or symbolic. Mainly, we distinguish two approaches, statistical and structural. In this paper, we are interested in a comparative study of these four methods: profile projection, zoning, cavities and freeman chain code. Digit recognition is carried out in this work through k nearest neighbors. We evaluated our scheme on handwritten samples of the MNIST database and we have achieved very promising results.

With rapid advances in nanotechnologies, Wireless Nanosensor Networks (WNSNs) have been emerged as a typical subclass of WSNs but at nano-scale. WNSN is a new communication fabric that allows nanonodes to connect to each other using new mechanisms such as molecular (or chemical) and electromagnetic. Unlike traditional WSNs, nanonodes are tiny with limited resources (e.g., computation, memory and energy) and therefore, are not able to communicate with the destination node using a single-hop scenario. The flooding mechanism, in which each node rebroadcasts the message once, was adopted for broadcasting in electromagnetic-based WNSNs. While the flooding mechanism is very simple, it is not efficient since most of the nanonodes are expected to rebroadcast a message at the same time, thus collisions are likely to occur. Furthermore, the excess number of rebroadcasts increases resources utilization that affects the network performance and increases energy consumption. In this paper, we investigate the flooding algorithm in the context of WNSNs and compare it with counter-based and probabilistic-based broadcasting mechanisms that are proposed for MANETs.