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Wireless sensor networks (WSN) are made up of an important number of sensors, called nodes, distributed in random way in a concerned monitoring area. All sensor nodes in the network are mounted with limited energy sources, which makes energy harvesting on top of the list of issues in WSN. A poor communication architecture can result in excessive consumption, reducing the network lifetime and throughput. Centralizing data collection and the introduction of gateways (GTs), to help cluster heads (CHs), improved WSN life time significantly. However, in vast regions, misplacement and poor distribution of GTs wastes a huge amount of energy and decreases network’s performances. In this work, we describe a reliable and dynamic with energy-awareness routing (RDEAR) protocol that provides a new GT’s election approach taking into consideration CHs density, transmission distance and energy. Applied on 20 different networks, RDEAR reduced the overall energy consumption, increased stability zone and network life time as well as other compared metrics. Our proposed approach increased network’s throughput up to 75.92% , 67.7% and 9.78% compared to the low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC) and static multihop routing (SMR), protocols, respectively.

Gesture recognition is an open phenomenon in computer vision, and one of the topics of current interest. Gesture recognition has many applications in the interpretation of sign language in deaf-mutes, one is in human-computer interaction, and the other is in immersive game technology. For this purpose, we have developed a model of image processing recognition of gestures, based on Artificial Neural Networks, starting from data collection, identification, tracking and classification of gestures, to the display of the obtained results. We propose an approach to contribute to the translation of sign language into voice/text format. In this paper, we present a Moroccan sign language recognition system using a Convolutional Neural Network (CNN). This system includes an important data set of more than 20 files. Each file contains 1,000 static images of each signal from several different angles that we collected with the camera. Different Sign Language models were evaluated and compared with the proposed CNN model. The proposed system achieved 99.33% and achieved the best performance with an accuracy rate of 98.7%.

This paper deals with the multi-objective optimization dispatch (MOOD) problem in a DC microgrid. The aim is to formulate the MOOD to simultaneously minimize the operating cost, pollutant emission level of (NOx, SO2 and CO2) and the power loss of conversion devices. Taking into account the equality and inequality constraints of the system. Two approaches have been adopted to solve the MOOD issue. The scalarization approach is first introduced, which combines the weighted sum method with price penalty factor to aggregate objective functions and obtain Pareto optimal solutions. Whilst, the Pareto approach is based on the implementation of evolutionary multi-objective optimization solution. Single and multi-objective versions of multi-verse optimizer algorithm are, respectively, employed in both approaches to handle the MOOD. For each time step, a fuzzy set theory is selected to find the best compromise solution in the Pareto optimal set. The simulation results reveal that the Pareto approach achieves the best performances with a considerable decrease of 28.96 $/day in the daily operating cost, a slight reduction in the power loss of conversion devices from 419.79 kWh to 419.29 kWh, and in less computational time. While, it is noticing a small increment in the pollutant emission level from 11.54 kg/day to 12.21 kg/day, for the daily microgrid operation. This deviation can be fully covered when comparing the cost related to the treatment of these pollutants, which is only 5.55 $/day, to the significant reduction in the operating cost obtained using the Pareto approach.

Faced to challenges of low power and lossy networks (LLN’s) as well as the nature of these networks without infrastructure, it remains the subject of several researches to deal with routing constraints and thus propose adapted routing protocols according to different applications. In this context, a protocol based on Ipv6 RPL has been standardized to meet the requirements of low power and lossy networks. RPL centralizes traffic to one or more nodes through the construction of a Destination Oriented Directed Acyclic Graph (DODAG) based on a specific objective function. Nevertheless, the objective function influences the behavior of the routing protocol, so that the design must follow the prerequisites of each application. The problem of standardized objective functions is found in their routing paths selection, they are not based on different criteria which leads to unoptimized choices of routes which affects the network quality of services . Thus, we propose in this paper a new objective function IRH-OF based on designed combined metric cmIRH for rank calculation. Our proposed method, injected on the core of Contiki Operating System, ensures a better quality of services compared to other objective functions. The results showed that the new objective function can maintain a packet delivery ratio higher than 98% regardless of density, decreases approximately with 45% the average power consumption of the network, achieves a less convergence time and latency.

This paper presents a novel optimization approach for a day-ahead power management and control of a DC microgrid (MG). The multi-objective optimization dispatch (MOOD) problem involves minimizing the overall operating cost, pollutant emission levels of (NOx, SO2 and CO2) and the power loss cost of the conversion devices. The weighted sum method is selected to convert the multi-objective optimization problem into a single optimization problem. Then, analytic hierarchy process (AHP) method is applied to determine the weight coefficients, according to the preference of each objective function. The system’s performance is evaluated under both grid connected and standalone operation mode, considering power balancing, high level penetration of renewable energy, optimal scheduling of charging/discharging of battery storage system, control of load curtailment and the system technical constraints. Ant lion optimizer (ALO) method is considered for handling MOOD, and the performance of the proposed algorithm is compared with other known heuristic optimization techniques. The simulation results prove the effectiveness and the capability of the developed approach to deal better with the coordinated control and optimization dispatch problem.They also revealed that economically running the MG system under grid connected mode can reduce the overall cost by around 4.70% compared to when it is in standalone operation mode.

The rise of the Internet of Things (IoT) and the diversity of its applications make the need for a flexible routing protocol able to face several constraints very essential. In this context, the RPL protocol was designed to meet the need for IoT classified as low power and lossy networks (LLN's). RPL relies on an objective function that takes into account a metric to implement its routing strategy in the network. On the other hand, the problem consists of a single routing metric that sometimes leads to the selection of non-optimized or static routes in the network. As a consequence, two main problems emerge, the first is the congestion created in the nodes due to the high number of packets to be relayed which leads to their loss, the second is the greedy energy consumption by these nodes which can conduct to quickly degrade their batteries, thus losing and causing loss the connectivity of several nodes in the network. To remedy this problem, a novel objective function for RPL FTC-OF has been implemented in the core of the embedded operating system CONTIKI, which combines three different routing metrics, aiming to create a balanced network while considering the quality of link on a short path, namely hop count, RSSI and a newly designed metric called Forwarded Traffic Metric (FTM). The proposed method, considered as a new balancing strategy, evaluated under the COOJA emulator, outperforms ETX and Energy based RPL protocols. It shows that it increases the rate of packet delivery, considerably reduces the power consumption of the network as well as an almost perfect balance of traffic managed by the relay nodes.

This paper addresses the issue of image and video face retrieval. The aim of this work is to be able to retrieve images and/or videos of specific person from a dataset of images and videos if we have a query image of that person. The methods proposed so far either focus on images or videos and use hand crafted features. In this work we built an end-to-end pipeline for both image and video face retrieval where we use convolutional neural network (CNN) features from an off-line feature extractor. And we exploit the object proposals learned by a region proposal network (RPN) in the online filtering and re-ranking steps. Moreover, we study the impact of finetuning the networks, the impact of sum-pooling and max-pooling, and the impact of different similarity metrics. The results that we were able to achieve are very promising.

Wireless sensor networks consist of a large number of nodes deployed randomly in an area of interest. Theses nodes have sensing, computation, and wireless communications capabilities. In another hand nodes have energy constraints since they are equipped by non-rechargeable batteries. To handle this issue, routing protocols consist of designing the network in order to collect and transmit data with less energy consumption. In this paper we presented a new Hybrid Energy Efficient Static routing protocol (HEESR), combining between clustering and multi-hop routing techniques. HEESR is dividing the network into several levels. For each round, it creates clusters, route the collected data through gateways called Independent Nodes, elected using a new dynamic approach and introduces Dormant nodes. Finally, the proposed HEESR have improved network’s life time, throughput and other compared metrics, in both homogeneous and heterogeneous networks, and prolonged network’s stability zone up to 98.4% compared to LEACH, 98% compared to DEEC and up to 40.5% compared to SMR.

The Virtual Private Network (VPN) service offered by the Multi-Protocol Label Switching Protocol (MPLS) is more and more solicited by clients thanks to its various advantages that it can offer especially concerning the security, the quality of service, and the flexibility. MPLS VPN relies on multiple protocols to function correctly and can be represented according to different architectures, each of which requires its own configuration. Besides, this technology is often used to interconnect a high number of sites which makes it challenging to deploy, manage, and supervise. Software Defined Network (SDN) is a paradigm, which, thanks to a centralized point (controller) can manage the MPLS VPN tunnels in an automatic, dynamic, and on-demand way between all the clients’ equipment. To manage the equipment by an SDN controller, they must be of the new generation supporting the OpenFlow protocol. The hybrid SDN paradigm allows Legacy equipment to be included in the orchestration as well, reducing OPEX and CAPEX costs. By the present work, we propose a new approach (SH-SDN) SDN Hybrid for the management of different architectures MPLS VPN based on a new graphical interface. The proposed approach allows not only to manage the MPLS VPN tunnels but also to ensure a reasonable quality of service level by performing an adaptive and an optimal load balancing across the multiple paths. The approach has been implemented and tested in a virtual test network consisting of different manufacturers (Cisco, Juniper, and HP). The results show that the SH-SDN approach offers a remarkable reduction in the MPLS VPN tunnel establishment time in a large network and a reasonable quality of service level of the transported applications through the multipath routing improved process.

This paper proposes a novel daily energy management system for optimization dispatch and operation control of a typical microgrid power system. The multi-objective optimization dispatch problem is formulated to simultaneously minimize the operating cost, pollutant emission level as well as the power loss of conversion devices. While satisfying the system load and technical constraints, ensure high penetration of renewable energy and optimal scheduling of charging/discharging of battery storage system based on a fuzzy logic approach. The weighted sum method is adopted to obtain Pareto optimal solutions, then a fuzzy set theory is employed to find the best compromise solution. Ant lion optimizer method is considered to solve the formulated problem. To prove the efficacy and robustness of the proposed algorithm, a comparison of the performance of ant lion optimizer algorithm with other known heuristic optimization techniques has been investigated. The results obtained show that the proposed algorithm outperforms the other heuristic techniques in solving the multi-objective optimization dispatch problem. They also reveal that a better compromise between the considered contradictory objective functions is achieved when priority is given to the generation of the internal microgrid’s sources with an equivalent contribution rate of 68.45% of generated power from both fuel cell and micro-turbine, whereas the contribution rate of external grid is limited to 11.72%.