Original article can be found here (source): Artificial Intelligence on Medium
MACHINE LEARNING DRIVEN DRONE ANALYTICS
Drones and Machine learning are inevitable sensations of the 21st century. Drones and machine learning opened numerous opportunities for academic and industrial research professionals. Drones have extended the freedom to operate and monitor the activities from remote locations. The machine learning implementation has reduced the number of challenges to drones besides enhancing the capabilities and opening the door to the different sectors. Drones and machine learning associations have resulted in fast and reliable outputs. The combination of Drones and machine learning has helped in real-time monitoring, data collection and processing, and prediction in the computer or wireless networks, smart cities, military, agriculture, and mining.
Drones are the replica of an aircraft with characteristics of portability, low cost, lightweight, low altitude flights, and snapping the objects on the ground without a pilot. Drone operators, from remote locations, control its functioning and operations. The existing trends if continued, then the numbers of the functions of the drone are expected to increase soon. The drone is of various types such as Fixed-Wing, Multirotor, Single Rotor, Rotary Blade, Nano, and Hybrid VTOL drones. However, there are three key components in UAV operation: base stations, aerial relays, and user devices.
AI has been considered the science of training machines to perform human tasks. There are many applications that AI has been involved in, including robotic vehicles, speech recognition, machine translation, and recently wireless communications. Moreover, a specific subset of AI is the techniques that are used for training machines in how to learn, which originates from a new framework known as Machine Learning(ML). ML can provide solutions in scenarios where a massive number of devices simultaneously requires access to the network’s resources in a dynamic, heterogeneous, and unpredictable way, e.g., in IoT communications. In this sense, intelligent management should be performed in the entire network to cope with the various demanding requirements of this novel type of service.
The scope is to adaptively and in real-time manage the network resources optimally. Therefore, Machine Learning algorithms have been proposed as an efficient approach for confronting all these contradictory challenges coming from the IoT ecosystem.
In general, Machine Learning is based on the pattern recognition framework and its main idea is to exploit correlation among a set of data and/or past good action sequences for adapting to the environmental changes without any kind of human intervention. The advantage offered by the Machine Learning framework in the wireless network operation is that it will enable network elements to monitor, learn, and predict various communication-related parameters, such as wireless channel behavior, traffic patterns, user context, and device locations. ML is primarily categorized as supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning (RL)
A wide range of analytics aspects exists that can be enhanced through Machine Learning solutions, including
- the development of accurate channel models in various environments and the mitigation of path-loss (PL) through prediction of the topology
- the tackling of severe interference from other UAVs and from ground nodes with timely training, using mobility and user association data
- the configuration of transmission parameters towards achieving specific performance targets.
Machine learning is simply a subset of artificial intelligence, which divides the data into training and testing sets to develop models for forecasting and prediction. There are some algorithms used for modeling and prediction in machine learning such as random forest, support vector machines, and k- neural network.
Drones and machine learning are useful in data mining from multi-dimensional maps, infrastructure development, and precision agriculture. For instance, the Artificial Neural Network (ANN) models are used in the rainfall-runoff process, prediction of evaporation, estimation of plant water uptake, plant classification based on leaves, crop yield prediction, and wetland mitigation for highway management. The UAV image data was used for the ecological process and structural modeling.
Drones can record multi-spectral photography, and its operator can control the imagery resolutions by flying at different altitudes. However, it is difficult to interpret high-resolution images without machine learning algorithms.
Random forest (RF) is a grouped learning technique, which uses bagging, or bootstrap aggregating for image classifications. With the RF method, spectral estimations can be provided. The random forest-based modeling has better performance than artificial neural networks and support vector machines. The extreme learning machine algorithms are used in regression and classification problems in quantitative remote sensing data. The advancement in machine learning algorithms, sensors, and IT technologies has opened the doors for drone applications in many sectors. The main sectors, however, are computer/ wireless networks, smart cities, military, agriculture, and mining.
The Liquid State Machine (LSM) was used as a machine learning algorithm for resource management in the UAV based network. A deep convolutional neural network was implemented for understanding the drone behaviors in a different controlling environment.
Multi-agent Q-learning and Echo State Network (ESN) was applied in controlling and management of drone assisted wireless networks. The drone used in smart cities and the military for achieving different results is rapidly growing. The drone platform and machine learning algorithms were used to build a graffiti clean-up system. The machine learning approach was used in detecting, tracking, and classifying flying objects whether piloted or unmanned.
The RF signals generated from the drone controller were used for drone detection and identification. Classification of drone types was done based on the detected Wi-Fi signals between the consumer drone and the controller by using CNN, Naive Bayes, and Markov models with the drone.
The combination of machine learning and drone imagery has proofed its usefulness in predicting soil moisture content irrigation and water management. Likewise, random forest, support vector machine, and multiple linear regression have been used for the prediction of vegetation production as well as for crop classification.
Classification of geological mining model based on surface feature detection was done through machine-learning algorithms using a support vector machine, k-nearest neighbor, random forest, Gradient Tree Boost, and Multiclass Relevance Vector Machine.
Machine learning and drone blending ensure improved precision, accuracy, and efficient outcomes in image classification and object detection. The use of drones for imagery, monitoring, surveying and mapping, precision agriculture, aerial remote sensing, and product delivery is expected to increase.
Among all of the algorithms, the random forest has the biggest share. It is the most frequently used algorithm due to its capability to handle noise in the data. The support vector machine has a second predominant status with a 21 % share. Convolution neural network and k-nearest neighbors’ implementation are eminent from their 16 % and 11 % shares. The other algorithms, which have been sporadically used are Naïve Bayes, liquid state, multi-agent learning, and artificial neural network.
Drones have limited capabilities in terms of energy resources, which makes the energy and power consumption optimization a very important factor in the overall network’s performance. In that sense, various Machine Learning solutions have been proposed that exploit in a near-optimal manner the network’s resources towards minimizing energy consumption. A group of drones is considered to fly at a certain altitude that allows them to provide communication coverage for ground users in a specific region.
When vast amounts of data from multiple sources are available, deep learning-based solutions can satisfactorily reveal useful correlations among heterogeneous data towards optimizing drone networks. Drones have limited processing capabilities or where dedicated, cloud/fog/edge infrastructure does not exist to process large amounts of data while connecting with and between the drones is not ensured, this approach might not be practically feasible.
Therefore, considering the varying processing capabilities of drones, their energy constraints, and the need for autonomous and distributed coordination, solutions entailing lower complexity and local estimation and processing of parameters might be more appropriate. This is the reason why machine-learning solutions came into existence.
Hope this article helped you gain some useful insights relating to machine learning-driven drone analytics tactics. Next time when you are about to perform any form of drone data analysis considers these Machine learning techniques. For further assistance, feedback and more details please reach us at http://takvaviya.com/