Scenarios Simulation and Analysis on Electric Power Planning Based on Multi-scale Forecast: A Case Study of Taoussa, Mali from 2020 to 2035

A long-term forecast study on the electricity demand of Taoussa of Mali is conducted in this paper, with various scenarios of socioeconomic and technological conditions. The analysis tool, which is applied in scenarios simulation, is the Model for Analysis of Energy Demand from the International Atomic Energy Agency. The analysis results are annual electricity demand and peak load forecast for the electrification from the period 2020 to 2035. During the planning period, the analysis results show that the electricity demand will increase to 49.40 MW (332.57 GWh) for the low scenario (LS), 66.46 MW (472.61 GWh) for the reference scenario (RS), and 89.47 MW (635 GWh) for the high scenario (HS). In addition, the total electricity demand increased at an average rate of 8.13% in the LS, 10.31% in the RS and 12.56% in the HS in all sectors. The electricity peak demand is expected to grow at 7.92%, 10.53% and 12.91% corresponding to the three scenarios; in this case, the system peak demand in 2035 will increase to 64.88 MW for the LS, 92.2 MW for the RS and 126.22 MW, the days of peak load are between 17th -23rd in May. The Industry sector will be the biggest electricity consumer of Taoussa area.

Security Enhancement in Smart Vehicle Using Blockchain-based Architectural Framework

The concept of interconnecting smart vehicles and advancements in automotive automation leads to beneficial outcomes, such as a reduction in road fatalities and congestion. However, including a chain of automation in the attack surface will expand the attack surface and expose the security of automobiles to malicious infiltration. The proposed methodology provides access to specific users while restricting the third party requests. Moreover, it also makes use of data exchange that takes place between the roadside units and vehicle to track the vehicle status without compromising the in-vehicle network. To ensure a valid and authentic communication, vehicles with a proper and verifiable record will only be allowed to exchange messages in the blockchain network. Using qualitative arguments, we have identified that the proposed work is resilient to identified attacks. Similarly, quantitative experimentation indicates that this methodology shows a storage size compatibility and suitable response time in realistic scenarios. Simulation results indicate that, the proposed work shows positive results to secure vehicular networks, vehicular forensics and trust management.

Caching-Aware Intelligent Handover Strategy for LEO Satellite Networks

Recently, many Low Earth Orbit (LEO) satellite networks are being implemented to provide seamless communication services for global users. Since the high mobility of LEO satellites, handover strategy has become one of the most important topics for LEO satellite systems. However, the limited on-board caching resource of satellites make it difficult to guarantee the handover performance. In this paper, we propose a multiple attributes decision handover strategy jointly considering three factors, which are caching capacity, remaining service time and the remaining idle channels of the satellites. Furthermore, a caching-aware intelligent handover strategy is given based on the deep reinforcement learning (DRL) to maximize the long-term benefits of the system. Compared with the traditional strategies, the proposed strategy reduces the handover failure rate by up to nearly 81% when the system caching occupancy reaches 90%, and it has a lower call blocking rate in high user arrival scenarios. Simulation results show that this strategy can effectively mitigate handover failure rate due to caching resource occupation, as well as flexibly allocate channel resources to reduce call blocking.

Modeling Rasmussen’s dynamic modeling problem: drift towards a boundary of safety

We build a system dynamics model based on a conceptual model originally proposed by safety scientist Jens Rasmussen to explore the dynamics of a safety system subject to pressures for performance improvement. Rasmussen described forces that generate a drift in the boundary of acceptable performance that can push the organization towards “flirting with the margin” and thus operate at very high risk of catastrophic safety failure. Simulations of the model faithfully replicate the behavior described by Rasmussen and others in a variety of scenarios. Simulation experiments further illuminate the potential for risky behavior and point towards some approaches to better system safety.

Wi-Fi Assisted Contextual Multi-Armed Bandit for Neighbor Discovery and Selection in Millimeter Wave Device to Device Communications

The unique features of millimeter waves (mmWaves) motivate its leveraging to future, beyond-fifth-generation/sixth-generation (B5G/6G)-based device-to-device (D2D) communications. However, the neighborhood discovery and selection (NDS) problem still needs intelligent solutions due to the trade-off of investigating adjacent devices for the optimum device choice against the crucial beamform training (BT) overhead. In this paper, by making use of multiband (μW/mmWave) standard devices, the mmWave NDS problem is addressed using machine-learning-based contextual multi-armed bandit (CMAB) algorithms. This is done by leveraging the context information of Wi-Fi signal characteristics, i.e., received signal strength (RSS), mean, and variance, to further improve the NDS method. In this setup, the transmitting device acts as the player, the arms are the candidate mmWave D2D links between that device and its neighbors, while the reward is the average throughput. We examine the NDS’s primary trade-off and the impacts of the contextual information on the total performance. Furthermore, modified energy-aware linear upper confidence bound (EA-LinUCB) and contextual Thomson sampling (EA-CTS) algorithms are proposed to handle the problem through reflecting the nearby devices’ withstanding battery levels, which simulate real scenarios. Simulation results ensure the superior efficiency of the proposed algorithms over the single band (mmWave) energy-aware noncontextual MAB algorithms (EA-UCB and EA-TS) and traditional schemes regarding energy efficiency and average throughput with a reasonable convergence rate.

The Potential Contamination Scenarios simulation in Case of the Water Chlorination Technological Process Violation in the Urban Environment

The research raise the question of the need to analyse and model the processes associated with the emission of the emergency-chemical hazardous substance (ECHS) in the urban environment. Because the assessment of the environmental pollution, the early and prompt prediction of the scale of contamination in the event of releases is the potent poisonous substance (PPS) into the environment in case of accidents (destructions) at chemically hazardous facilities remains an urgent issue. We emphasize that in order to ensure the life of any settlement, there is a need for the availability of drinking water that meets sanitary standards and hygienic rules. Chlorination is traditionally used in most of the country's settlements. To take timely measures to eliminate accidents with the emission of chlorine, the most accurate results of calculating the main indicators are necessary, such as the depth of the zone of contamination of PPS, the amount of the poisonous substance in primary and secondary clouds, the area of contamination, etc. The method of calculating these indicators is based on the use of reference tables and the coefficient method, which are convenient enough for the manual method. However, in emergencies, when serious threats to the life and health of people arise, and the situation can deteriorate rapidly, there is an acute shortage of time for a general assessment of the situation, making decisions on the organization of the rescue measures. It is obvious that the staff simply will not have enough time for scrupulous calculations, and a stressful situation, in addition, with a high degree of probability will provoke serious errors and inaccuracies.

Event Generation for Emergency Scenarios Simulation

Natural and technological disasters have been part of the daily life of societies in recent decades, causing harm and disruption in different parts of the world where they occur. Emergency management is the discipline that aims to promote support to the populations involved in a disaster, in order to mitigate the consequences of such disaster. Modelling and simulation plays a key role in decision-making and training in face of complex systems and procedures. Organizations responsible for responding to different types of disaster need tools that can improve the training and preparation of disaster support teams, creating scenarios as close to reality as possible. This chapter reports the creation of a solution for a scenario generation system capable of producing events similar to those verified in disasters, with a view to conducting training sessions, including near-real-time tabletop exercises and the planning and execution of field exercises, with the aim of decision-making training for relief teams in emergency situations.

An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise

Aiming towards state estimation and information fusion for nonlinear systems with heavy-tailed measurement noise, a variational Bayesian Student’s t-based cubature information filter (VBST-CIF) is designed. Furthermore, a multi-sensor variational Bayesian Student’s t-based cubature information feedback fusion (VBST-CIFF) algorithm is also derived. In the proposed VBST-CIF, the spherical-radial cubature (SRC) rule is embedded into the variational Bayes (VB) method for a joint estimation of states and scale matrix, degree-of-freedom (DOF) parameter, as well as an auxiliary parameter in the nonlinear system with heavy-tailed noise. The designed VBST-CIF facilitates multi-sensor fusion, allowing to derive a VBST-CIFF algorithm based on multi-sensor information feedback fusion. The performance of the proposed algorithms is assessed in target tracking scenarios. Simulation results demonstrate that the proposed VBST-CIF/VBST-CIFF outperform the conventional cubature information filter (CIF) and cubature information feedback fusion (CIFF) algorithms.