The spread of false news on social networks is a major challenge in the digital age across various sectors, encompassing technology, politics, public health, and finance. This paper introduces an ontology-based method that combines text and image analysis to evaluate the accuracy of news stories in the context of social media. We investigate the role of social engineering tactics in crafting and dispersing fake news and advocate for a comprehensive multi-contextual perspective that covers content, source, social media, psychological, and impact aspects. Using OWL (Web Ontology Language), we present an ontology framework for assessing fake news, providing a structured approach to analyze text, visuals, audio, audience behavior, source credibility, and news propagation patterns. This framework serves as a foundation for advanced detection systems, contributing to the fight against digital misinformation.

Land Use and Land Cover (LULC) information helps to analyze future trends and is essential for environmental management and sustainable planning. Time-series satellite images are employed in this study to forecast changes in LULC. Deep learning frameworks have been widely used for modeling dynamic LULC changes at the regional level. However, improving the accuracy of the existing prediction models is necessary. This paper proposes an integrated convolutional neural network (CNN) and long short-term memory network (LSTM) known as a hybrid CNN-LSTM model to address the fine-scale LULC prediction requirement. The efficiency of the proposed approach was examined using LULC data for the Dakshina Kannada District of Karnataka State, India. The proposed model achieved an overall accuracy of 95.11 % and a kappa coefficient of 0.92, based on the ground truth data for 2014. The model’s predictions for 2035, based on data from 2005 to 2014, revealed the following trends: Urbanization exhibited a pattern of rapid expansion and increased growth. The integrated CNN-LSTM model extracted spatial and temporal features for effectively predicting LULC changes. Infrastructure development, population density, and enhanced economic activities were the major driving factors of changes in LULC for the study region. Robust LULC change forecasting will strengthen LULC evaluations, aid in understanding complex land-use systems, and empower decision-makers to formulate effective land management strategies in the coming years.

Unit electricity pricing is of vital importance in an electric grid network. It is essential to charge the customers in a fair manner. Traditional pricing models are found to be inadequate in the ability to charge customers fairly due to a lack of support for real-time communication between customers and electricity providers. With the introduction of smart devices in the electric grid domain, the real-time gathering of information is a seamless process. Such an electric network that uses smart devices is called a smart grid. In a smart grid network, electricity providers can monitor the electricity usage pattern of customers in a real-time manner, which can then be analyzed to determine the appropriate prices. To analyze the customer’s history of usage and price the electricity in a real-time manner, the computation must be performed with minimal latencies. Adoption of a fog computing layer in the smart grids can aid in the attainment of this goal. In this article, we propose a novel method for the pricing of electricity. In our approach, the electric demand of a household is predicted based on their past usage patterns. Users are then clustered into different bins based on their demands, and an evolutionary algorithm is used to generate the prices for the users present in different bins in a real-time manner to ensure the maximum attainable profit to a service provider.

Given the limited tool support for energy-saving strategies during the design phase of android applications, developing battery-aware, location-based android applications is a non-trivial task for developers. To this end, we propose eGEN, consisting of (1) a Domain-Specific Modeling Language (DSML) and (2) a code generator to specify and create native battery-aware, location-based mobile applications. We evaluated eGEN by instrumenting the generated battery-aware code in five location-based, open-source android applications and compared the energy consumption with non-eGEN versions. The experimental results show 188 mA (8.34% of battery per hour) of average reduction in battery consumption while showing only 97 meters of degradation in location accuracy over three kilometers of a cycling path. Hence, we see this tool as a first step in helping developers write battery-aware code in location-based android applications. The GitHub repository with source code and all artifacts is available at https://github.com/Kowndinya2000/egen, and the tool demo video at https://youtu.be/Iadfh4cCw8I.

Abstract Distributed Cloud environments are now resorting to Cloud applications composed of heterogeneous microservices. Cloud service providers strive to provide high quality of service (QoS) and response time is one of the key QoS attributes for microservices. The dynamism of microservice ecosystems necessitates runtime adaptations and microservices rescheduling to avoid performance degradation. Existing works target rescheduling in hypervisor‐based systems, while ignoring the influence of configuration parameters of container‐based microservices. In an effort to address these challenges, this article describes a novel microservice rescheduling framework, throttling and interaction‐aware anticorrelated rescheduling for microservices, to proactively perform rescheduling activities whilst ensuring timely service responses. Based on periodic monitoring of the performance attributes, the framework schedules container migrations. Considering the exponentially large solution space, a metaheuristic approach based on multiverse optimization is developed to generate the near‐optimal mapping of microservices to the datacenter resources. Experimental results indicate that our framework provides superior performance with a reduction of up to 13.97% in the average response time, when compared with systems with no support for rescheduling.

Abstract There is an increasing interest shown by researchers and developers in reducing the battery consumption of Android applications. Recently, the battery optimization features such as doze mode, app standby, background execution limits, and background location limits were introduced in the form of API changes. According to the API changes, application developers have to change their source code to manage the behavioral changes caused by operating system limitations. These battery optimization features are evolving rapidly, and the apps show unexpected behaviors until updating the source code. Also, developers find it difficult to cope with the changes. Therefore, there is a need to understand the behavioral changes, application developer's perceptions, and response patterns on the API changes to plan upcoming battery optimization features. In this article, we have collected the relevant GitHub issues from 225 open‐source Android repositories and performed a thematic analysis of collected data. This study analyzes the 391 related issues to answer three research questions. This study's important finding is that developers often post issues related to delayed app notifications, inconsistent background location updates, and suspended background tasks, and so on. We found that library developers are showing a quick response to API changes compared with application developers.

The number of Android applications using location information has increased significantly in recent years. Over time, there have been many improvements made to the location application programme interfaces (APIs), providing newer challenges and difficulties to the developers. Therefore, there is a need to summarise the existing knowledge and to highlight the unsolved issues to bring them to the attention of expert developers. The authors used the non‐negative matrix factorisation (NMF) method to identify the topics discussed by the developers on stack overflow. They found the following ten topics: fundamental, background service, global positioning system (GPS) provider, application error, location updates, programming aspects, GPS alternatives, location settings, NULL location, and location testing. In addition, they performed a manual analysis to add more qualitative insights into the results. They applied the NMF method on 3165 question posts and produced ten related topics. This study aims at organising the knowledge about location‐sensing strategies by answering three relevant research questions. They also analysed the most popular and unanswered topics in recent years. An important finding of this study is that the changes that occurred in the Google Location APIs have had a significant impact on the location‐sensing strategies followed by the developers.

Abstract Integration of Internet of Things (IoT) with industries revamps the traditional ways in which industries work. Fog computing extends Cloud services to the vicinity of end users. Fog reduces delays induced by communication with the distant clouds in IoT environments. The resource constrained nature of Fog computing nodes demands an efficient placement policy for deploying applications, or their services. The distributed and heterogeneous features of Fog environments deem it imperative to consider the reliability performance parameter in placement decisions to provide services without interruptions. Increasing reliability leads to an increase in the cost. In this article, we propose a service placement policy which addresses the conflicting criteria of service reliability and monetary cost. A multiobjective optimisation problem is formulated and a novel placement policy, Cost and Reliability‐aware Eagle‐Whale (CREW), is proposed to provide placement decisions ensuring timely service responses. Considering the exponentially large solution space, CREW adopts Eagle strategy based multi‐Whale optimisation for taking placement decisions. We have considered real time microservice applications for validating our approaches, and CREW has been experimentally shown to outperform the existing popular multiobjective meta‐heuristics such as NSGA‐II and MOWOA based placement strategies.

The improper use of GPS and location-related APIs may result in abnormal battery drain in Android applications.Over the last few years, the developers' discussions on improving energy efficiency have been increased.In this paper, we mine StackOverflow to analyze and summarize the characteristics of developers' discussions of managing energy and accuracy-related requirements of location-based Android applications.We extracted 11,911 questions from StackOverflow and filtered 320 relevant questions to answer four research questions.We conducted a manual thematic analysis on relevant questions.Our study shows that the developers are concerned about energy consumption, but are unclear about their preferences as energy and accuracy evolved as conflicting requirements.

Nowadays the Cloud Computing paradigm has become the defacto platform for deploying and managing user applications. Monolithic Cloud applications pose several challenges in terms of scalability and flexibility. Hence, Cloud applications are designed as microservices. Application scheduling and energy efficiency are key concerns in Cloud computing research. Allocating the microservice containers to the hosts in the datacenter is an NP-hard problem. There is a need for efficient allocation strategies to determine the placement of the microservice containers in Cloud datacenters to minimize Service Level Agreement violations and energy consumption. In this paper, we design a Reinforcement Learning-based Microservice Allocation (RL-MA) approach. The approach is implemented in the ContainerCloudSim simulator. The evaluation is conducted using the real-world Google cluster trace. Results indicate that the proposed method reduces both the SLA violation and energy consumption when compared to the existing policies.

In the past few decades, smartphones and Global Positioning System(GPS) devices have led to the popularity of Location Based Services. It is crucial for large MNCs to get a lot of data from people and provide their services accordingly. However, on the other side, the concern of privacy has also increased among the users, and they would like to hide their whereabouts. The rise of data consumption and the hunger for faster network speed has also led to the emergence of new concepts such as the Fog Computing. Fog computing paradigm extends the storage, networking, and computing facilities of the cloud computing towards the edge of the networks while removing the load on the server centers and decreasing the latency at the edge device. The fog computing will help in unlimited growth of location services and this adoption of fog computing calls for the need for more secure and robust algorithms for location privacy. One of the ways we can alter the information regarding the location of the user is Location Obfuscation. This can be done reversibly or irreversibly. In this paper, we address the problem of location privacy and present a solution based on the type of data that has to be preserved (in our case, it is distance). A mobile application has been designed and developed to test and validate the feasibility of the proposed obfuscation techniques for the Fog computing environments.

Summary Microservice architecture style has been gaining wide impetus in the software engineering industry. Researchers and practitioners have adopted the microservices concepts into several application domains such as the internet of things, cloud computing, service computing, and healthcare. Applications developed in alignment with the microservices principles require an underlying platform with management capabilities to coordinate the different microservice units and ensure that the application functionalities are delivered to the user. A multitude of approaches has been proposed for the various tasks in microservices‐based systems. However, since the field is relatively young, there is a need to organize the different research works. In this study, we present a comprehensive review of the research approaches directed toward microservice architectures and propose a multilevel taxonomy to categorize the existing research. The study also discusses the different distributed computing paradigms employing microservices and identifies the open research challenges in the domain.

Large-scale distributed data processing has gained significant momentum in research in the past decade. With the introduction of MapReduce, many frameworks have been developed that either implement MapReduce or provide additional functionalities useful in a larger domain. While the framework introduced in the MapReduce paper performs batch-processing of data, Apache Storm performs real-time computation on data. Storm does this with the help of Topologies, and the constituents of the Topology are developed using General-purpose Programming Languages (GPL). A Domain-specific Language (DSL) can provide a higher level of abstraction over GPLs and model the specialized features of a particular domain in a better way. In this paper, we propose the development of Storm Topology generator (Stormgen), a DSL for Storm Topology development, and show how the specifications of this DSL can be utilized during the code generation of exact Storm Topology components in Java. The parser and code generator for Stormgen's syntax are developed using the Eclipse Modelling Framework. The practical use of Stormgen is illustrated with a case study which considers the modelling of a Topology for the Word Count application.