To reach the goal of sustainable agriculture, smart farming is taking advantage of the Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) paradigm. These smart farms are designed to be run by interconnected devices and vehicles. Some enormous potentials can be achieved by the integration of different IoT technologies to achieve automated operations with minimum supervision. This paper outlines some major applications of IoT and UAV in smart farming, explores the communication technologies, network functionalities and connectivity requirements for Smart farming. The connectivity limitations of smart agriculture and it’s solutions are analysed with two case studies. In case study-1, we propose and evaluate meshed Long Range Wide Area Network (LoRaWAN) gateways to address connectivity limitations of Smart Farming. While in case study-2, we explore satellite communication systems to provide connectivity to smart farms in remote areas of Australia. Finally, we conclude the paper by identifying future research challenges on this topic and outlining directions to address those challenges.

Contributions: An adoption framework to include generative artificial intelligence (GenAI) in the university curriculum. It identifies and highlights the role of different stakeholders (university management, students, staff, etc.) during the adoption process. It also proposes an objective approach based upon an evaluation matrix to assess the success and outcome of the GenAI adoption. Background: Universities worldwide are debating and struggling with the adoption of GenAI in their curriculum. GenAI has impacted our perspective on traditional methods of academic integrity and the scholarship of teaching, learning, and research. Both the faculty and students are unsure about the approach in the absence of clear guidelines through the administration and regulators. This requires an established framework to define a process and articulate the roles and responsibilities of each stakeholder involved. Research Questions: Whether the academic ecosystem requires a methodology to adopt GenAI into its curriculum? A systematic approach for the academic staff to ensure the students’ learning outcomes are met with the adoption of GenAI. How to measure and communicate the adoption of GenAI in the university setup? Methodology: The methodology employed in this study focuses on examining the university education system and assessing the opportunities and challenges related to incorporating GenAI in teaching and learning. Additionally, it identifies a gap and the absence of a comprehensive framework that obstructs the effective integration of GenAI within the academic environment. Findings: The literature survey results indicate the limited or no adoption of GenAI by the university, which further reflects the dilemma in the minds of different stakeholders. For the successful adoption of GenAI, a standard framework is proposed 1) for effective redesign of the course curriculum; 2) for enabling staff and students; and 3) to define an evaluation matrix to measure the effectiveness and success of the adoption process.

In recent years, education institutions have offered a wide range of course selections with overlaps. This presents significant challenges to students in selecting successful courses that match their current knowledge and personal goals. Although many studies have been conducted on Recommender Systems (RS), a review of methodologies used in course RS is still insufficiently explored. To fill this literature gap, this paper presents the state of the art of methodologies used in course RS along with the summary of the types of data sources used to evaluate these techniques. This review aims to recognize emerging trends in course RS techniques in recent research literature to deliver insights for researchers for further investigation. We provide a systematic review process followed by research findings on the current methodologies implemented in different course RS in selected research journals such as: collaborative, content-based, knowledge-based, Data Mining (DM), hybrid, statistical and Conversational RS (CRS). This study analyzed publications between 2016 and June 2020, in three repositories; IEEE Xplore, ACM, and Google Scholar. These papers were explored and classified based on the methodology used in recommending courses. This review has revealed that there is a growing popularity in hybrid course RS and followed by DM techniques in recent publications. However, few CRS-based course RS were present in the selected publications. Finally, we discussed future avenues based on the research outcome, which might lead to next-generation course RS.

Error Correction Schemes (ECSs) significantly contribute to enhancing reliability and energy efficiency of Wireless Sensor Networks (WSNs). This review paper offers an overview of the different types of ECS used in communication systems and a synopsis of the standards for WSN. We also discuss channels and network models for WSN as they are crucial for efficient ECS design and implementation. The literature review conducted on the proposed energy consumption and efficiency models for WSN indicates that existing research work has not considered Single Hop Asymmetric Structure (SHAS) with high performing Error Correcting Codes (ECCs). We present a review on proposed ECS for WSN based on three criteria: Forward Error Correction (FEC), adaptive error correction techniques, and other techniques. Based on our review work, we found that there are limited works on ECS design on a realistic network model i.e., a modified multi-hop WSN model. Finally, we offer future research challenges and opportunities on ECS design and implementation for WSN.

This paper explores how generative artificial intelligence (GenAI) technologies, such as ChatGPT 4o and other AI-based conversational models, can be applied to flipped learning pedagogy to achieve enhanced learning outcomes for students. By applying Bloom’s taxonomy to intentionally align educational objectives to the key phases of flipped learning, our study proposes a model for assigning learning activities to pre-class, in-class, and post-class contexts that can be enhanced by the integration of GenAI. In the pre-class phase, GenAI tools can facilitate personalised content delivery, enabling students to grasp fundamental concepts at their own pace. During class, the interactions between students, teacher, and GenAI encourage collaborative learning and real-time feedback. Post-class activities utilise GenAI to reinforce knowledge, provide instant feedback, and support continuous learning through summarisation and content generation. Furthermore, our model articulates the synergies between the three key actors: interactions between students and teachers, learning support provided by GenAI to students, and use of GenAI by teachers to enhance their teaching strategies. These human–AI interactions fundamentally reshape the flipped learning experience, making it more adaptive, engaging, and supportive of the development of 21st-century skills such as critical thinking, collaboration, communication, and creativity.