Bio-inspired Approaches for Engineering Adaptive Systems

AbstractAdaptive systems are composed of different heterogeneous parts or entities that interact and perform actions favouring the emer- gence of global desired behavior. In this type of systems entities might join or leave without disturbing the collective, and the system should self-organize and continue performing their goals. Furthermore, entities must self-evolve and self-improve by learn- ing from their interactions with the environment. The main challenge for engineering these systems is to design and develop distributed and adaptive algorithms that allow system entities to select the best suitable strategy/action and drive the system to the best suitable behavior according to the current state of the system and environment changes. This paper describes existing work related to the development of adaptive systems and approaches and shed light on how features from natural and biological systems could be exploited for engineering adaptive approaches

An efficient broadcasting routing protocol WAODV in mobile ad hoc networks

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)

An Immune Inspired-based Optimization Algorithm: Application to the Traveling Salesman Problem

The clonal selection is a mechanism used by the natural immune system to select cells that recognize the antigens to proliferate. The proliferated cells are subject to an affinity maturation process, which improves their affinity to the selective antigens. The concept of clonal selection is a vitally important one to the success of the human immune system, and it provides an excellent example of the principles of selection at work. The Positive and negative selection is another interesting mechanism in the immune system that work together to both retain cells that recognize the self peptides, while also removing cells that recognize any self peptides. In this paper, a cloning-based algorithm inspired by the clonal and the positive/negative selection mechanism of the natural immune system is presented. This algorithm is inherently parallel and the cloning strategy employs greedy criteria which lends to an adaptive approach. The well known TSP is used to illustrate the approach with experimental comparison with Ant approach. Simulations demonstrate that this approach generates good solutions to traveling salesman problem and greatly improve the convergence speed compared to the Ant-based optimization approach

Embedded Real-Time Speed Forecasting for Electric Vehicles: A Case Study on RSK Urban Roads

International audienceDuring the past ten years, worldwide efforts have been pursuing anambitious policy of sustainable development, particularly in theenergy sector. This ambition was revealed by noticeable progress inthe deployment and development of infrastructures for theproduction of renewable electrical energy. These infrastructurescombined with the deployment of wired and wireless communicationscould support research actions in the field of connectedelectro-mobility. Also, this progress was manifested by thedevelopment of electric vehicles (EV), penetrating ourtransportation roads more and more. They are considered among thepotential solutions, which are envisaged to further reduce roadtransport’s greenhouse gas emissions, relying on low-carbon energyproduction. However, the uncertainty caused by both external roaddisturbances and drivers’ behavior could influence the predictionof upcoming power demands. These latter are mainly affected by theunpredictability of the electric vehicles’ speed on transportationroads. In this work, we introduce an energy management platform,which interfaces with in-vehicle components, using a developedembedded system, and external services, using IoT and big datatechnologies, for efficient battery power use. The platform wasdeployed in real-setting scenarios and tested for EV speedprediction. In fact, we have used driving data, which have beencollected on Rabat-Salé-Kénitra (RSK) urban roads by our Twizy EV.A multivariate Long Short Term Memory (LSTM) algorithm wasdeveloped and deployed for speed forecasting. The effectiveness ofLSTM was evaluated against well-known algorithms: Auto RegressiveIntegrated Moving Average (ARIMA), Convolutional Neural Network(CNN) and Convolutional LSTM (ConvLSTM). Experiments have beenconducted using two approaches; the whole trajectory dataset andsegmented trajectory datasets to train the models. Theexperimentation results show that LSTM outperforms the other usedalgorithms in terms of forecasting the speed, especially when usingthe trajectory segmentation approach

A Hybrid Approach for State-of-Charge Forecasting in Battery-Powered Electric Vehicles

International audienceNowadays, electric vehicles (EV) are increasingly penetrating thetransportation roads in most countries worldwide. Many efforts areoriented toward the deployment of the EVs infrastructures,including those dedicated to intelligent transportation andelectro-mobility as well. For instance, many Moroccan organizationsare collaborating to deploy charging stations in mostly allMoroccan cities. Furthermore, in Morocco, EVs are tax-free, andtheir users can charge for free their vehicles in any station.However, customers are still worried by the driving range of EVs.For instance, a new driving style is needed to increase the drivingrange of their EV, which is not easy in most cases. Therefore, theneed for a companion system that helps in adopting a suitabledriving style arise. The driving range depends mainly on thebattery’s capacity. Hence, knowing in advance the battery’sstate-of-charge (SoC) could help in computing the remaining drivingrange. In this paper, a battery SoC forecasting method isintroduced and tested in a real case scenario on Rabat-Salé-Kénitraurban roads using a Twizy EV. Results show that this method is ableto forecast the SoC up to 180 s ahead with minimal errors and lowcomputational overhead, making it more suitable for deployment inin-vehicle embedded systems