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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.

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.

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.

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.

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.

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.

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.

Electricity demand increases every year and there is a need to improve the existing power grid. Smart Grid concept was developed by Institutes and research agencies to address this need. This new concept can be generally considered as an intelligent power grid system that implements distributed energy. Gathering, coordinating operation and control of multiple distributed energy resources, energy storages, loads and set of users pose a great challenge. Effective management of smart grid will have a benefit of reduced cost of energy, reduced stress on the electric energy infrastructure, and improved energy efficiency. This paper reviews the research and studies on mathematical models used to optimize energy management in smart grid. The paper compares and discusses various fitness functions used in the literature. Genetic Algorithm is explored and brief overviews of other methods are discussed.

Broadcasting is a fundamental operation in a mobile ad hoc network (MANETs) protocols. The flooding is the simplest mechanism used for message broadcasting in MANETs in which a message is rebroadcasted, by each node, exactly once. Despite its simplicity, it can cause high redundancy retransmission that could generate messages contention and collision. However, the issue is finding a way to minimize the number of redundantly received messages in order to save transmission energy while maintaining good latency and high reachability. Several enhanced versions of the flooding mechanism have been proposed to alleviate this issue by inhibiting some nodes from rebroadcasting. The selection of relay nodes is a major design consideration in all enhanced versions. In this paper, a dynamic threshold-based probabilistic scheme (DTP) is introduced. In DTP, the threshold value at the nodes is dynamically adjusted using only information from neighboring nodes. Results are presented and show the efficiency of DTP in terms of reachability, saved rebroadcast, and average latency.

Currently mobile devices like smartphones are equipped with high performance processors, memory, sensors and storage. Therefore they can do a lot of what traditional desktop and laptop computers do. Their emergence has led to many applications especially which are offered by the cloud computing. This technology is called mobile cloud computing. MCC combines the features of both mobile computing and cloud computing. Users of Mobile can get rich experience of various services from their own mobile applications that run on cloud servers. The only barrier which prevents those users to adopt mobile cloud computing is that there are risks in terms of security and privacy of the data and services. Because, mobile cloud computing has limitations in terms of data storage, bandwidth, heterogeneity, scalability, availability, reliability, and privacy [5]. So the security solution for it has become a research focus. In this paper, we are more interested in data security in mobile cloud computing environment. We propose a hybrid architecture based on Encryption as a Service. It empowers the cloud clients to be in control of their cryptographic operations and keys that they deploy in the cloud, independently of the cloud provider. Firstly, we outline cloud computing technology, then we explore mobile cloud computing environment and we analyze some encryption algorithms. Finally we propose our achitecure to solve the problem of privacy in MCC.

Mobile devices have become indispensable tools equipped with different types of sensors that are changing our lifestyles. combined with wireless sensor networks, the information collected will help solve problems that citizens face each day while living together and getting around in public areas. This paper presents our proposal regarding the use of the distributed platform “Kalimucho” to develop context-aware applications for smart cities where different heterogeneous devices will work together to improve the quality of life of citizens.