The emerging and vast adoption of the Internet of Things (IoT) has sprung a plethora of research works regarding the potential benefits in smart agriculture. A popular implementation involves the deployment of Wireless Sensor Networks (WSNs), which embed low energy consumption sensory nodes to capture the critical environmental parameters prevailing on the farms. However, to manage the ever-increasing volumes of raw data successfully, new approaches must be explored. Under this scope, current work reports on the design and development of an IoT system, having in mind the case of olive groves, which are considered the dominant sector for agricultural activity in the Mediterranean Basin. The system incorporates the cloud/fog computing paradigm to equip the olive growers with a low-cost solution for accurate, reliable, and almost real-time monitoring of their crops. Its core is based on a three-layered network architecture, capable of dynamically balancing the generated load, by pushing cloud-elastic resources to the underlying fog network. As such, the premise of the approach lies in the conforming character of the system that allows for targeted alterations to its operational functionality to meet stringent latency and traffic load environmental monitoring constraints. To evaluate the performance of the proposed architecture, a demo prototype is developed and deployed in the facilities of the Ionian University. Experimental results illustrate the efficiency, flexibility, and scalability of the approach in terms of latency, achieving response time reduction across all platforms, a subject of the utmost importance when it comes to precision agriculture of the future. Moreover, it is shown that the system is capable of dynamic functionality adaptation, to meet network traffic load constraints, achieving high throughput (on average 95%) and addressing potential environmental dangers to olive oil production.

Purpose: Mobile devices (smartphones, tables etc.) have become the de facto means of accessing the internet. While traditional Web browsing is still quite popular, significant interaction takes place via native mobile apps that can be downloaded either freely or at a cost. This has opened the door to a number of issues related to privacy protection since the smartphone stores and processes personal data. The purpose of this paper is to examine the extent of access to personal data, required by the most popular mobile apps available in Google Play store. In addition, it is examined whether the relevant procedure is in accordance with the provisions of the new EU Regulation. Design/methodology/approach: The paper examines more than a thousand mobile apps, available from the Google Play store, with respect to the extent of the requests for access to personal data. In particular, for each available category in Google Play store, the most popular mobile apps have been examined both for free and paid apps. In addition, the permissions required by free and paid mobile apps are compared. Furthermore, a correlation analysis is carried out aiming to reveal any correlation between the extent of required access to personal data and the popularity and the rating of each mobile app. Findings: The findings of this paper suggest that the majority of examined mobile apps require access to personal data to a high extent. In addition, it is found that free mobile apps request access to personal data in a higher extent compared to the relevant requests by paid apps, which indicates strongly that the business model of free mobile apps is based on personal data exploitation. The most popular types of access permissions are revealed for both free and paid apps. In addition, important questions are raised in relation to user awareness and behavior, data minimization and purpose limitation for free and paid mobile apps. Originality/value: In this study, the process and the extent of access to personal data through mobile apps are analyzed. Although several studies analyzed relevant issues in the past, the originality of this research is mainly based on the following facts: first, this work took into account the recent Regulation of the EU in relation to personal data (GDPR); second, the authors analyzed a high number of the most popular mobile apps (more than a thousand); and third, the authors compare and analyze the different approaches followed between free and paid mobile apps.

This paper provides a review of classification methods for irony and sarcasm in textual data. It explores different approaches to detecting irony and sarcasm, their definitions, distinguishing features, and detection methodologies. The study examines a range of datasets used in irony and sarcasm detection research, including short-text datasets from social media platforms and long-text datasets from product reviews and discussion forums. Additionally, the paper discusses the various features employed in sarcasm detection experiments, such as lexical, pragmatic, hyperbole, semantic, syntactic, sentiment, and contextual features. It also explores the classification methodologies used. The paper concludes by analyzing each classification method and highlighting the latest trends in irony and sarcasm detection.

Mobile phones have become a vital part of our daily lives with users relying on them for a variety of activities. Users don't hesitate to provide their personal data to free or paid mobile applications, in order to use them with no interruption. However, the amount of personal data being exchanged has raised concerns, resulting in the introduction of regulations for protecting personal data at many countries. A regulation in this direction that was enacted in Europe is the General Data Protection Regulation known as GDPR that came into effect in May 2018. This study examines the impact of GDPR on Google Play applications by comparing a dataset of metrics collected in 2017 to a post-GDPR dataset gathered in 2022. The focus of this research is to analyze the effect of GDPR on mobile app usage and its associated metrics by comparing the datasets of the two above periods. Our analysis shows that progress has been made towards achieving GDPR compliance, but there is still a considerable distance to be covered before reaching full compliance. A key observation is that the number of permissions used by apps has not been reduced even after the GDPR implementation.

In this article, we explore the impact that social media has on information consumption and community dialogue. We discuss concepts like filter bubbles, echo chambers, and polarization. Although filter bubbles tailor content to individual preferences, echo chambers reinforce ideological conformity by surrounding individuals with like-minded peers. These phenomena intensify the polarization that society is facing by intensifying divisions. However, research challenges the conventional understanding of filter bubbles, echo chambers, and polarization, suggesting that our online communities are less polarized and that conflict and disagreement play a greater role in online discussions. We also explore the Friedkin and Johnsen model, which offers us insights into opinion formation within social networks, highlighting internal and external conflict and disagreement dynamics. The nature of social media amplifies disagreement and conflict, underscoring the need for strategies that promote diversity. Research should explore opinion models and societal dynamics that address platform design in shaping information ecosystems.

Mobile apps are essential for communication, transactions and leisure and frequently rely on access to personal data. This study examines Google Play’s Data Safety section and declared permissions five years after the GDPR came into force, focusing on how developers disclose data collection, sharing, security practices and deletion controls. We use metadata from 49,578 Android apps and analyze self-reported disclosures in relation to permission categories, app categories, installs and user ratings. The results show that free apps request broader permission access than paid ones and that declared permission use has gradually increased over time. In addition, 25.44% of the sampled apps had not completed any part of the Data Safety section and non-completion was associated with app age, installation band and pricing model. Among apps with completed relevant Data Safety section disclosures, 11% of developers explicitly declared that data are not encrypted in transit and 34% explicitly declared that no user-initiated data deletion mechanism is available. Category-level differences in declared data collection and sharing were modest, while the relationship between permission breadth and user ratings was small. Overall, the findings indicate that structured disclosure mechanisms can improve visibility of privacy-related information, but do not necessarily ensure its completeness or consistency.