The Internet of Energy (IoE) paradigm introduces new challenges and opportunities for efficient power routing in decentralized energy networks. A novel approach is implemented to enhance power routing efficiency within the IoE framework by integrating adaptive Q-learning techniques with a maintenance phase. The proposed distributed Self-Healing Protocol (SHP) utilizes discovered paths during the maintenance phase instead of recomputing a path to transmit a Power Packet (PP) in case of a fault. Our proposed Self-Healing Q-Learning Routing protocol aims to optimize energy delivery by dynamically adapting routing decisions and addressing system maintenance in real-time. This power routing protocol operates without centralized control, leveraging autonomous behaviors for system resilience and performance optimization. It covers all scenarios, including Multiple Source Single Load (MSSL), Multiple Source Multiple Load (MSML), and Single Source Multiple Load (SSML) scenarios. This approach is validated using MATLAB simulations.

International audience

The Constrained Application Protocol (CoAP) is a lightweight communication protocol designed by the Internet Engineering Task Force (IETF) for wireless sensor networks and Internet-of-Things (IoT) devices. The reliability mechanism in CoAP is based on retransmissions after timeout expiration and on an exponential backoff procedure which is designed to be simple and adapted to constrained devices. In this research work, we propose a new exact analytical model to analyze the performance of CoAP in lossy wireless networks modeled by the well-known Gilbert-Elliott two-state Markov process. We also show how to compute several performance metrics using closed form expressions such as the observed loss ratio, goodput, and the delay before success with a time complexity no more than O(r) with r is the maximum re-transmission limit. This study provides insights about improving CoAP recovery mechanism and highlights the properties -- including the limitations -- of CoAP. Also, it presents guidelines to tune CoAP parameters dynamically in order to adapt to network losses caused by interference and mobility. The model is validated using the realistic environment Cooja/Contiki OS where theoretical and experimental results match very well.

Multiple types of Demand Response Programs (DRPs) are offered within the same infrastructure. Multiple objectives of technical, economic and marketing order are targeted. Defining a model with weights for the various objectives and the related Key Performance Indicators (KPIs) is of a high importance. A structured model can be achieved through the assessment of an adequate market design with adequate dynamic pricing schemes to be offered to consumers, coupled with reducing costs at various levels and optimizing the technical indices while using business intelligence and big data. The originality of this paper resides in the simulation of a model combining multiple DRPs with multiple objectives and their assigned weights, hence the elasticity of dynamic parameters and criteria selection to achieve the compromise. It includes the proposal of dynamic pricing schemes for consumers’ detected clusters while optimizing costs and considering technical constraints. The optimal solution is the result of combining an assessment process based on historical data coupled with intensive what if scenarios to serve the planning and optimization phase, thus its application will be guaranteed. The validation is preformed through a simulation on Matlab using multi-objective optimization clustering based with assigned weights.

Assessment and qualification of consumers' behavior play an important role in the selection of the suitable Demand Response Programs (DRPs) achieving their engagement and satisfaction. Targeting the assessed consumers' clusters with adequate and elastic pricing schemes ensures their participation. The model offers the full visibility on the dynamicity of pricing and demands for the various simultaneous clusters' DRPs operating in a synchronized manner. The originality of this paper resides in the classification of consumers' behaviors according to the criteria and the elasticity of the multiple dynamic offered pricing schemes for the clusters along with their impacts on each other. An optimal solution is reached through assessment, qualification, planning, benefits' visibility on various proposals and relocation. Intensive what if scenarios assist in the decision making process of the optimal selection in a planned phase. Thus, contracts' terms and conditions suiting the consumers and the electrical utility are arranged. The method is validated through a simulation on Matlab using clustering and multi-objective optimization.

The Constrained Application Protocol (CoAP) is a lightweight core protocol used for communication between devices in the Internet-of-Things. The CoAP standard defines a simple congestion control mechanism based mainly on retransmissions after timeouts and a binary exponential back-off procedure with randomization. However, the default parameters of this mechanism can affect negatively the efficiency of the protocol in severe network conditions. In this paper, we propose new exact mathematical models to study the performance of CoAP in lossy networks. This study provides useful insights in order to tune CoAP parameters and also highlights CoAP properties and limitations so that future better mechanisms can be designed adequately.

Playing a significant role in the operation of the modern electric grid, Demand Response Programs (DRPs) balance the demand on the consumer level with the supply through the deployment of specific measures. An important undesirable phenomenon may occur when simultaneously launched DRPs on a same network interact negatively between each other. Therefore, an optimal coordination is necessary in order to optimize a preset objective function. Within this context, and as a new approach for solving this issue, this paper suggests to perform an optimal clustering applied on the network gathered data. Based on published results, the importance of synchronization between different DRPs is proved. It is shown how the suggested clustering would improve the synchronization process. As a demonstration for this new approach, clustering simulations are applied on a set of a real data built through measurements performed on the distribution network of a university campus.

This paper presents a review of the recent advances in matrix converter topologies used with the permanent magnet synchronous generator (PMSG) in wind power generation systems. Dedicated literatures introduce different topologies inspired by the conventional matrix converter. Two-stage matrix converters known as sparse matrix converters are compared to the conventional matrix converter in terms of implementation complexity, cost, power rating and voltage ratio. Single-phase matrix converters topologies are implemented in high power wind applications in two different modular concepts: the reduced matrix concept for offshore wind farms and the multi-modular high power matrix converter. On the other hand, several modulation techniques applied to the matrix converter-PMSG scheme are briefly presented.

This paper presents the design of a MATLAB simulator of a hybrid renewable energy system applied for energy generation in rural regions. The system is simulated using MATLAB. It consists of a set of photovoltaic panels, a DFIG wind farm, a pumped water storage/generation station and it is designated to supply residential/industrial loads. A transmission line connects this system to the main power grid. Although conventional models are used, the work originality consists of performing an optimal control of sources and loads in order to keep power balancing between generation and consumption in both connected and islanded modes.

In this paper, we propose a Hybrid Multi-Channel Multi-hop Medium Access Control (HMM-MAC) meeting the safety and non-safety requirements in Vehicular Ad-hoc Networks when no infrastructure is present (V2V communication) with only a single transceiver at each vehicle. The simulation results showed that our scheme could achieve a higher delivery ratio of road safety messages and a high probability to select a free transmission channel in a certain region and during a certain time.

This paper deals with solving robot cooperative task conflict using robot assistive behavior processing. These conflicts could occur in reactive multi mobile robot task. To manage such conflicts, each robot must assist the other units from their own capacities. A role method based on capacity adjustment is proposed. A role is a specific robot assistive behavior. Conflicts are then compensated by role switching. The same processing can be used in man-robot interaction. In this case, the robot has to define the efficient role in order to assist the man. A specific multi mobile robot conflict problem is studied: a constraint case of a reactive handling task with two mobile robots. This is a critical task since the object transported is only held by robot compression. Any degraded case, hardware failure or tactical conflict occurrences, would lead to a task deadlock. To define the global assistive role, on-line fuzzy reinforcement is used to adjust basic behaviors. In the studied case, no explicit communication is used to define the relationship between the robots in order to be generalized to man-machine cooperation. Our goal is to develop a strategy to manage interaction with handicapped persons. We studied a robotic rollator. In this case, the assistive role of rollator has to be adjusted in order to compensate the man's task

Evaluation systems for handicapped people through adapted interface are used to measure of people motor disability parameters and to provide assistance. In this work, a friendly reconfigurable evaluation and assistance platform for handicapped people is presented. A set of interfaces (mouse, joysticks, etc.) is used for controlling the pointing system. Different systems can be controlled (PCs cursor, robot Khepera, electric wheelchair). The main characteristics of this platform are the availability of several interface devices, exercises interaction with 2D models or 3D models, the saving of experimental data, the easy configuration of different scenarios, and the remote access capabilities. The system is illustrated through an implementation example and preliminary results are shown on a child with cerebral palsy