Unmanned Aerial Vehicle (UAV) is a promising technology that can serve as aerial base stations to assist Internet of Things (IoT) networks, solving various problems such as extending network coverage, enhancing network performance, transferring energy to IoT devices (IoTDs), and perform computationally-intensive tasks of IoTDs. Heterogeneous IoTDs connected to IoT networks have limited processing capability, so they cannot perform resource-intensive activities for extended periods. Additionally, IoT network is vulnerable to security threats and natural calamities, limiting the execution of real-time applications. Although there have been many attempts to solve resource scarcity through computational offloading with Energy Harvesting (EH), the emergency and vulnerability issues have still been under-explored so far. This paper proposes a blockchain and multi-agent deep reinforcement learning (MADRL) integrated framework for computation offloading with EH in a multi-UAV-assisted IoT network, where IoTDs obtain computing and energy resources from UAVs. We first formulate the optimization problem as the joint optimization problem of computation offloading and EH problems while considering the optimal resource price. And then, we model the optimization problem as a Stackelberg game to investigate the interaction between IoTDs and UAVs by allowing them to continuously adjust their resource demands and pricing strategies. In particular, the formulated problem can be addressed indirectly by a stochastic game model to minimize computation costs for IoTDs while maximizing the utility of UAVs. The MADRL algorithm solves the defined problem due to its dynamic and large-dimensional properties. Finally, extensive simulation results demonstrate the superiority of our proposed framework compared to the state-of-the-art.

The need to fight the progressive negative impact of fake news is escalating, which is evident in the strive to do research and develop tools that could do this job. However, a lack of adequate datasets and good word embeddings have posed challenges to make detection methods sufficiently accurate. These resources are even totally missing for “low-resource” African languages, such as Amharic. Alleviating these critical problems should not be left for tomorrow. Deep learning methods and word embeddings contributed a lot in devising automatic fake news detection mechanisms. Several contributions are presented, including an Amharic fake news detection model, a general-purpose Amharic corpus (GPAC), a novel Amharic fake news detection dataset (ETH_FAKE), and Amharic fasttext word embedding (AMFTWE). Our Amharic fake news detection model, evaluated with the ETH_FAKE dataset and using the AMFTWE, performed very well.

Graphs are a tremendously suitable data representation that models the relationships of entities in many application domains, such as recommendation systems, machine learning, computational biology, social network analysis, and other application domains. Graphs with many vertices and edges have become quite prevalent in recent years. Therefore, graph computing systems with integrated various graph partitioning techniques have been envisioned as a promising paradigm to handle large-scale graph analytics in these application domains. However, scalable processing of large-scale graphs is challenging due to their high volume and inherent irregular structure of the real-world graphs. Hence, industry and academia have recently proposed graph partitioning and computing systems to efficiently process and analyze large-scale graphs. The graph partitioning and computing systems have been designed to improve scalability issues and reduce processing time complexity. This paper presents an overview, classification, and investigation of the most popular graph partitioning and computing systems. The various methods and approaches of graph partitioning and diverse categories of graph computing systems are presented. Finally, we discuss future challenges and research directions in graph partitioning and computing systems.

The agri-food sector is undergoing a comprehensive transformation as it transitions towards net zero. To achieve this, fundamental changes and innovations are required, including changes in how food is produced and delivered to customers, new technologies, data and physical infrastructures, and algorithmic advancements. In this paper, we explore the opportunities and challenges of deploying AI-based data infrastructures for sustainability in the agri-food sector by focusing on two case studies: soft-fruit production and brewery operations. We investigate the potential benefits of incorporating Internet of Things (IoT) sensors and AI technologies for improving the use of resources, reducing carbon footprints, and enhancing decision-making. We identify user engagement with new technologies as a key challenge, together with issues in data quality arising from environmental volatility, difficulties in generalising models, including those designed for carbon calculators, and socio-technical barriers to adoption. We highlight and advocate for user engagement, more granular availability of sensor, production, and emissions data, and more transparent carbon footprint calculations. Our proposed future directions include semantic data integration to enhance interoperability, the generation of synthetic data to overcome the lack of real-world farm data, and multi-objective optimisation systems to model the competing interests between yield and sustainability goals. In general, we argue that AI is not a silver bullet for net zero challenges in the agri-food industry, but at the same time, AI solutions, when appropriately designed and deployed, can be a useful tool when operating in synergy with other approaches.