Improved information flow topology for vehicle convoy control

A vehicle convoy is a string of inter-connected vehicles moving together for mutual support, minimizing traffic congestion, facilitating people safety, ensuring string stability and maximizing ride comfort. There exists a trade-off among the convoy's performance indices, which is inherent in any existing vehicle convoy. The use of unrealistic information flow topology (IFT) in vehicle convoy control, generally affects the overall performance of the convoy, due to the undesired changes in dynamic parameters (relative position, speed, acceleration and jerk) experienced by the following vehicle. This thesis proposes an improved information flow topology for vehicle convoy control. The improved topology is of the two-vehicle look-ahead and rear-vehicle control that aimed to cut-off the trade-off with a more robust control structure, which can handle constraints, wider range of control regions and provide acceptable performance simultaneously. The proposed improved topology has been designed in three sections. The first section explores the single vehicle's dynamic equations describing the derived internal and external disturbances modeled together as a unit. In the second section, the vehicle model is then integrated into the control strategy of the improved topology in order to improve the performance of the convoy to two look-ahead and rear. The changes in parameters of the improved convoy topology are compared through simulation with the most widely used conventional convoy topologies of one-vehicle look-ahead and that of the most human-driver like (the two-vehicle look-ahead) convoy topology. The results showed that the proposed convoy control topology has an improved performance with an increase in the intervehicular spacing by 19.45% and 18.20% reduction in acceleration by 20.28% and 15.17% reduction in jerk by 25.09% and 6.25% as against the one-look-ahead and twolook- ahead respectively. Finally, a model predictive control (MPC) system was designed and combined with the improved convoy topology to strictly control the following vehicle. The MPC serves the purpose of handling constraints, providing smoother and satisfactory responses and providing ride comfort with no trade-off in terms of performance or stability. The performance of the proposed MPC based improved convoy topology was then investigated via simulation and the results were compared with the previously improved convoy topology without MPC. The improved convoy topology with MPC provides safer inter-vehicular spacing by 13.86% refined the steady speed to maneuvering speed, provided reduction in acceleration by 32.11% and a huge achievement was recorded in reduction in jerk by 55.12% as against that without MPC. This shows that the MPC based improved convoy control topology gave enough spacing for any uncertain application of brake by the two look-ahead or further acceleration from the rear-vehicle. Similarly, manoeuvering speed was seen to ensure safety ahead and rear, ride comfort was achieved due to the low acceleration and jerk of the following vehicle. The controlling vehicle responded to changes, hence good handling was achieved

A Preliminary Study and Implementing Algorithm Using Finite State Automaton for Remote Identification of Drones

Electronic remote identification (ER-ID) is a new radio frequency (RF) technology that is initiated by the Federal Aviation Authorities (FAA). For security reasons, traffic control, and so on, ER-ID has been applied for drones by the FAA to enable them to transmit their unique identification and location so that unauthorized drones can be identified. The current limitation of the existing ER-ID algorithms is that the application is limited to the Wi-Fi and Bluetooth wireless controllers, which results in a maximum range of 10–20 m for Bluetooth and 50–100 m for Wi-Fi. In this study, a mathematical computing technique based on finite state automaton (FSA) is introduced to expand the range of the ER-ID RF system and reduce the energy required by the drone to use the technology. A finite number of states have been designed to include a larger range of wireless network techniques, enabling the drones to be recognized while they are further away and in remote areas. This is achieved by including other means of RF channels, such as 4G/5G, Automatic Dependent Surveillance-Broadcast (ADS-B), long range Internet of things (IoT), and satellite communications, in the suggested ER-ID algorithm of this study. The introduced algorithm is tested via a case study. The results showed the ability to detect drones using all types of available radio frequency communication systems (RF-CS) while also minimizing the consumed energy. Hence, the authorities can control the licensed drones by using available RF-CS devices, such as Bluetooth and Wi-Fi, which are already widely used for mobile phones, as an example

Development of LoRa-Sigfox IoT device for long distance applications

A key feature that is required from the IoT-enabled devices is low power and long-range communication. In this paper, a prototype IoT device that is enabled with LoRa and Sigfox communication technology is developed and tested. The prototype device is built using the Pycom Fipy module and tested in a LoRaWAN setup and Sigfox setup. A test was conducted to determine the performance of the LoRa propagation in different environments and at different times of the day. The experimental data shows the coverage and diurnal performance using the IoT device. A distance of 1.17km was achieved in the open space using the LoRa while the propagation was highly impacted in the vegetation area with a distance of.512 km. The test for the Sigfox showed good coverage over a distance of 10 km. The results show the device can be used for both LoRa and Sigfox coverage test

Decision support platform for production of chili using IoT, cloud computing, and machine learning approach

The chili crop is largely grown in several regions of the world, especially in Asian and African countries. It is a major source of income for both small- and large-scale farmers. Unfortunately, chili farmers have to contend with the challenge of pests and diseases and the need for timely decisions to have a bountiful production. To solve this problem, this paper proposes a chili-decision support platform (chili-DSP) that can help farmers detect diseases, and nutrient deficiency and make timely decisions. The proposed system integrates the internet of things, cloud computing, and data analytics technologies. The framework and architecture of the proposed chili-DSP are presented in this paper and the preliminary results using the convolutional neural network (CNN) for the classification of chili are presented. The result shows that CNN provides an accurate prediction of the learned data set and can be extended to larger data set for real-time classification of chili diseases. The chili-DSP is expected to provide a comprehensive feature and support that will help the chili farmers enhance the production of chili while minimizing losses