The state of roads may sometimes be difficult to perceive due to intense climate conditions, absence of road signs, or simply human inattention, which may be harmful to both vehicles and drivers. The automatic monitoring of the road states represents a promising solution to warn drivers about the status of a road in order to protect them from injuries or accidents. In this paper, we present a novel application for data collection regarding road states. Our application entitled “Road Scanner” allows onboard users to tag four types of segments in roads: smooth, bumps, potholes, and others. For each tagged segment the application records multimodal data using the embedded sensors of a smartphone. The collected data concerns mainly vehicle accelerations, angular rotations, and geographical positions recorded by the accelerometer, the gyroscope, and the GPS sensor, respectively, of a user phone. Moreover, a medium-size dataset was built and machine learning models were applied to detect the right label for the road segment. Overall, the results were very promising since the SVM classifier (Support Vector Machines) has recorded an accuracy rate of 88.05%.

Data traffic and network congestion remain some of the main challenges in IoT systems. In this manuscript, we propose an innovative numerical method aimed at reducing the number of required sensors while maintaining data quality and integrity. The method proposed is an optimization technique based on a variant of the step method coupled with the interpolation technique. It allows the re-sizing of the existing IoT architecture by removing the latent redundancy within the system. By optimizing resources, our method addresses the critical issues of data traffic and energy efficiency in IoT networks. The algorithm intelligently reduces the number of sensors needed without compromising the accuracy and reliability of the collected data. Hence, it mitigates the adverse effects of network congestion and excessive energy demands. After determining the new IoT architecture, we tested the network on unseen data. The results indicate that the relative error follows a consistent trend observed in the training set, further demonstrating the robustness of the method. This optimization ensures efficient and effective IoT operations, ultimately contributing to the development of more sustainable and scalable IoT infrastructures. Through this method, we provide a viable solution to some of the most pressing challenges faced by modern IoT networks.

In the last few years, the number of users of the internet becomes more than the number of humans on the planet themselves. Indeed, the big parts of surfers are intelligent devices used in normal life or by researches. Connecting smart devices to internet have provided a huge amount of data which make analyzing, storing and managing it as a difficult task. On the other hand, cloud computing provides a great potential to manage and store data. Consequently, many researchers propose to use cloud computing to manage data collected by things. However, the use of cloud computing with things face many challengeslike: minimize the use of energy, provide the best quality of service, protect and facilitate the access to data, use data effectively and avoid unnecessary data. In this research, we concentrate on how to use artificial intelligence to segregate unnecessary data collected by things, to avoid unnecessary charging of storage and processing resources of things as well as of cloud.

Since of the advent of Industry 4.0, embedded systems have become an indispensable component of our life. However, one of the most significant disadvantages of these gadgets is their high power consumption. It was demonstrated that making efficient use of the device’s central processing unit (CPU) enhances its energy efficiency. The use of the particle swarm optimization (PSO) over an embedded environment achieves many resource problems. Difficulties of online implementation arise primarily from the unavoidable lengthy simulation time to evaluate a candidate solution. In this paper, an embedded two-level PSO (E2L-PSO) for intelligent real-time simulation is introduced. This algorithm is proposed to be executed online and adapted to embedded applications. An automatic adaptation of the asynchronous embedded two-level PSO algorithm to CPU is completed. The Flexible Job Shop Scheduling Problem (FJSP) is selected to solve, due to its importance in the Industry 4.0 era. An analysis of the run-time performance on handling E2L-PSO over an STM32F407VG-Discovery card and a Raspberry Pi B+ card is conducted. By the experimental study, such optimization decreases the CPU time consumption by 10% to 70%, according to the CPU reduction needed (soft, medium, or hard reduction).