Development of an Autonomous Aerial Toolset for Agricultural Applications

According to the United Nations, the world population is expected to grow from its current 7 billion to 9.7 billion by the year 2050. During this time, global food demand is also expected to increase by between 59% and 98% due to the population increase, accompanied by an increasing demand for protein due to a rising standard of living throughout developing countries. [1] Meeting this increase in required food production using present agricultural practices would necessitate a similar increase in farmland; a resource which does not exist in abundance. Therefore, in order to meet growing food demands, new methods will need to be developed to increase the efficiency of farming, thereby increasing yield from the present land. One way in which this problem can be solved is through the usage of autonomous aerial systems to scout for problems which could potentially affect the crop yield – such as nutrient deficiency, water stress, or diseases. Once located, this data can be used to determine the proper treatment for the field to alleviate the problem. Through this process, resources can be reduced to the required minimum, while problems affecting the crop yield will still be corrected, allowing greater production with a lower amount of resources. This project on the application of Unmanned Aerial Vehicles (UAV’s) to the field of agriculture consisted of two phases. First, a study was conducted on the required background to define the problem statement and what solutions were available for this application. This consisted of first defining the operations within agriculture where UAV’s could be used to increase efficiency, and then the sensors, hardware, and software these operations would require. The remainder of the project consisted of evaluating the tools which could be utilized to develop such a solution. Primarily, the project focused on software tools – programming software, simulation environments, and machine learning algorithms – which could be utilized by future students to develop a functional hardware and software toolchain for the research of autonomous systems for agricultural applications. After analyzing these development solutions, a set of tools was selected which showed promise in the creation of a functional solution. It was demonstrated that the core functions required for a UAV-based agricultural solution – navigation, perception, and feature detection – could be implemented within these systems, implying that they could be integrated into a full solution. As the tools were selected to ensure the developed algorithms would be transferable to physical platforms, this additionally supports a physical system could also be developed. The present work is part of the Autonomous Systems Lab which belongs to the WKU Center for Energy Systems. The author hopes that this project contributes to the advancement of the curriculum within the engineering department and serves as a foundation for future students developing autonomous systems, perception, and applied artificial intelligence at WKU

Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement

With the increasing usage, scale, and complexity of Deep Learning (DL) models, their rapidly growing energy consumption has become a critical concern. Promoting green development and energy awareness at different granularities is the need of the hour to limit carbon emissions of DL systems. However, the lack of standard and repeatable tools to accurately measure and optimize energy consumption at a fine granularity (e.g., at method level) hinders progress in this area. This paper introduces FECoM (Fine-grained Energy Consumption Meter), a framework for fine-grained DL energy consumption measurement. FECoM enables researchers and developers to profile DL APIs from energy perspective. FECoM addresses the challenges of measuring energy consumption at fine-grained level by using static instrumentation and considering various factors, including computational load and temperature stability. We assess FECoM's capability to measure fine-grained energy consumption for one of the most popular open-source DL frameworks, namely TensorFlow. Using FECoM, we also investigate the impact of parameter size and execution time on energy consumption, enriching our understanding of TensorFlow APIs' energy profiles. Furthermore, we elaborate on the considerations, issues, and challenges that one needs to consider while designing and implementing a fine-grained energy consumption measurement tool. This work will facilitate further advances in DL energy measurement and the development of energy-aware practices for DL systems

Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks. Such challenges can be potentially overcome by integrating communication, computing, caching, and control (i4C) technologies. In this survey, we first give a snapshot of different aspects of the i4C, comprising background, motivation, leading technological enablers, potential applications, and use cases. Next, we describe different models of communication, computing, caching, and control (4C) to lay the foundation of the integration approach. We review current state-of-the-art research efforts related to the i4C, focusing on recent trends of both conventional and artificial intelligence (AI)-based integration approaches. We also highlight the need for intelligence in resources integration. Then, we discuss integration of sensing and communication (ISAC) and classify the integration approaches into various classes. Finally, we propose open challenges and present future research directions for beyond 5G networks, such as 6G.Comment: This article has been accepted for inclusion in a future issue of China Communications Journal in IEEE Xplor

Enabling Real-Time AI Edge Video Analytics

This paper introduces a novel distributed AI model for managing in real-time, edge based intelligent analytics, such as the ones required for smart video surveillance. The novelty relies on distributing the applications in several decomposed functions which are linked together, creating virtual chain func- tions, where both computational and communication limitations are considered. Both theoretical analysis and simulation analysis in a real-case scenario have shown that the proposed model can enable real-time surveillance analytics on a low-cost edge network. Finally, a caching mechanism is proposed and evaluated, reducing further the operational costs of the edge network

Federated Learning on Edge Sensing Devices: A Review

The ability to monitor ambient characteristics, interact with them, and derive information about the surroundings has been made possible by the rapid proliferation of edge sensing devices like IoT, mobile, and wearable devices and their measuring capabilities with integrated sensors. Even though these devices are small and have less capacity for data storage and processing, they produce vast amounts of data. Some example application areas where sensor data is collected and processed include healthcare, environmental (including air quality and pollution levels), automotive, industrial, aerospace, and agricultural applications. These enormous volumes of sensing data collected from the edge devices are analyzed using a variety of Machine Learning (ML) and Deep Learning (DL) approaches. However, analyzing them on the cloud or a server presents challenges related to privacy, hardware, and connectivity limitations. Federated Learning (FL) is emerging as a solution to these problems while preserving privacy by jointly training a model without sharing raw data. In this paper, we review the FL strategies from the perspective of edge sensing devices to get over the limitations of conventional machine learning techniques. We focus on the key FL principles, software frameworks, and testbeds. We also explore the current sensor technologies, properties of the sensing devices and sensing applications where FL is utilized. We conclude with a discussion on open issues and future research directions on FL for further studie

Visualization of AI Systems in Virtual Reality: A Comprehensive Review

This study provides a comprehensive review of the utilization of Virtual Reality (VR) for visualizing Artificial Intelligence (AI) systems, drawing on 18 selected studies. The results illuminate a complex interplay of tools, methods, and approaches, notably the prominence of VR engines like Unreal Engine and Unity. However, despite these tools, a universal solution for effective AI visualization remains elusive, reflecting the unique strengths and limitations of each technique. We observed the application of VR for AI visualization across multiple domains, despite challenges such as high data complexity and cognitive load. Moreover, it briefly discusses the emerging ethical considerations pertaining to the broad integration of these technologies. Despite these challenges, the field shows significant potential, emphasizing the need for dedicated research efforts to unlock the full potential of these immersive technologies. This review, therefore, outlines a roadmap for future research, encouraging innovation in visualization techniques, addressing identified challenges, and considering the ethical implications of VR and AI convergence.Comment: 19 page

A Mini Review on the utilization of Reinforcement Learning with OPC UA

Reinforcement Learning (RL) is a powerful machine learning paradigm that has been applied in various fields such as robotics, natural language processing and game playing achieving state-of-the-art results. Targeted to solve sequential decision making problems, it is by design able to learn from experience and therefore adapt to changing dynamic environments. These capabilities make it a prime candidate for controlling and optimizing complex processes in industry. The key to fully exploiting this potential is the seamless integration of RL into existing industrial systems. The industrial communication standard Open Platform Communications UnifiedArchitecture (OPC UA) could bridge this gap. However, since RL and OPC UA are from different fields,there is a need for researchers to bridge the gap between the two technologies. This work serves to bridge this gap by providing a brief technical overview of both technologies and carrying out a semi-exhaustive literature review to gain insights on how RL and OPC UA are applied in combination. With this survey, three main research topics have been identified, following the intersection of RL with OPC UA. The results of the literature review show that RL is a promising technology for the control and optimization of industrial processes, but does not yet have the necessary standardized interfaces to be deployed in real-world scenarios with reasonably low effort.Comment: submitted to INDIN'2