Application of Artificial Intelligence in IoT Security for Crop Yield Prediction

This research explores the application of Artificial Intelligence (AI) in the Internet of Things (IoT) for crop yield prediction in agriculture. IoT devices, like sensors and drones, collect data on temperature, humidity, soil moisture, and crop health. AI algorithms process and integrate this data to provide a comprehensive view of the agricultural environment.AI-driven anomaly detection helps identify threats to crop yield, such as pests, diseases, and adverse weather conditions. Predictive analytics, based on historical and real-time data, forecast crop yield for informed decision-making in irrigation and fertilization.AI-powered image recognition detects early signs of pests and diseases, aiding timely treatment to prevent crop losses. Resource optimization allocates water and fertilizers efficiently, minimizing waste and environmental impact.AI-driven decision support systems offer personalized recommendations for ideal planting schedules and crop rotations, maximizing yield. Autonomous farming integrates AI into machinery for precision tasks like planting and monitoring.Secure communication protocols protect sensitive agricultural data from cyber threats, ensuring data integrity and privacy

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters

Context is a vital concept in various fields, such as linguistics, psychology, and computer science. It refers to the background, environment, or situation in which an event, action, or idea occurs or exists. Categorization of context involves grouping contexts into different types or classes based on shared characteristics. Physical context, social context, cultural context, temporal context, and cognitive context are a few categories under which context can be divided. Each type of context plays a significant role in shaping our understanding and interpretation of events or actions. Understanding and categorizing context is essential for many applications, such as natural language processing, human-computer interaction, and communication studies, as it provides valuable information for interpretation, prediction, and decision-making. In this paper, we will provide an overview of the concept of context and its categorization, highlighting the importance of context in various fields and applications. We will discuss each type of context and provide examples of how they are used in different fields. Finally, we will conclude by emphasizing the significance of understanding and categorizing context for interpretation, prediction, and decision-making

A Study of Factors Influencing the Adoption of Artificial Intelligence in Crisis Management

This paper presents a study on the Factors Influencing the Adoption of Artificial Intelligence (AI) in Crisis Management. The research identifies 28 AI usage factors categorized into seven groups: Large-Scale Machine Learning, Deep Learning, Reinforcement Learning, Robotics, Computer Vision, Natural Language Processing, and Internet of Things. The study conducted a questionnaire survey among 281 employees at the UAE National Crisis and Emergency Management Authority, using purposive sampling to assess their opinions regarding the impact of these usage factors on the adoption of AI in crisis management. The collected data underwent descriptive analysis to determine the ranking of AI usage factors within each of the seven groups. In terms of group rankings, Robotic emerged as the top-ranking factor, followed by Reinforcement Learning. Large-Scale Machine Learning occupied the next position, succeeded by Natural Language Processing, Deep Learning, Internet of Things, and Computer Vision, which held the lowest rank. Furthermore, when examining the correlation between these usage factor groups, it was discovered that most of them exhibited strong positive correlations, with correlation coefficients ranging from 0.634 to 0.934. This indicates that changes in one variable are associated with predictable changes in another variable. While this information can be instrumental in understanding relationships and making predictions, it does not establish a causal relationship

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.publishedVersio

The Role of Artificial Intelligence in Next-Generation Wireless Networks - an Overview of Technological and Law Implications

openThis thesis explores how artificial intelligence (AI) will benefit next-generation wireless networks focusing both on the technological aspects and legal implications. At first, a summary of AI history is provided, together with an overview of the different AI methodologies. Among them, the thesis focuses on machine learning-based approaches and in particular neural network algorithms for wireless networks. The second chapter examines how AI can support wireless network management and explores the advantages of adopting this new paradigm as a substitute for current non-data-driven approaches. Next, we discuss the technical challenges that should be addressed for the practical integration of AI within wireless networks, starting from the huge amount of data needed to properly configure AI methodologies and the high computational demand. Emerging approaches that will allow overcoming the above-mentioned challenges, such as, e.g., the placement of computational servers near the base stations and the adoption of federated learning techniques, are also discussed. In the third chapter, we examine the cybersecurity risks that arise with the adoption of AI in wireless networks, and the necessary regulations that will help address these risks. A vision of the future of AI in wireless networks, and a discussion of the open research challenges from technological and legal points of view conclude the thesis.This thesis explores how artificial intelligence (AI) will benefit next-generation wireless networks focusing both on the technological aspects and legal implications. At first, a summary of AI history is provided, together with an overview of the different AI methodologies. Among them, the thesis focuses on machine learning-based approaches and in particular neural network algorithms for wireless networks. The second chapter examines how AI can support wireless network management and explores the advantages of adopting this new paradigm as a substitute for current non-data-driven approaches. Next, we discuss the technical challenges that should be addressed for the practical integration of AI within wireless networks, starting from the huge amount of data needed to properly configure AI methodologies and the high computational demand. Emerging approaches that will allow overcoming the above-mentioned challenges, such as, e.g., the placement of computational servers near the base stations and the adoption of federated learning techniques, are also discussed. In the third chapter, we examine the cybersecurity risks that arise with the adoption of AI in wireless networks, and the necessary regulations that will help address these risks. A vision of the future of AI in wireless networks, and a discussion of the open research challenges from technological and legal points of view conclude the thesis