In recent times, Wireless Sensor Network (WSN) technology has received significant attention owing to its numerous applications in various mission critical services, such as healthcare, military monitoring, public safety systems, and forest monitoring. However, sensor node’s low computational power and inadequate advancements in battery design hinder resource-rigorous applications from fully exploiting WSN capabilities. The trend of shifting computations and storage to remote clouds offers the opportunity to integrate WSNs into the cloud to mitigate their limitations. The Sensor Cloud augments WSN capabilities of remote sensing and monitoring while exploiting resource-rich cloud infrastructure. To date, numerous solutions have been proposed to handle the integration concerns of WSNs with the cloud. However, a comprehensive study that covers various Sensor Cloud aspects in terms of architecture, network dynamics, heterogeneity, communication patterns, data management, and security, is still lacking. To fill this gap, state-of-the-art sensor cloud integration frameworks are analyzed in this article. Moreover, a detailed thematic taxonomy is presented to classify existing sensor cloud frameworks while focusing in service oriented and client server architectures. Related features and critical aspects of existing frameworks are inspected through a qualitative analysis based on the parameters selected from the literature and extracted from the taxonomy. Several research opportunities in this research domain are suggested to assist researchers in identifying and designing optimal resource sensor cloud integration frameworks to host emerging applications.

Android applications provide benefits to mobile phone users by offering operative functionalities and interactive user interfaces. However, application crashes give users an unsatisfactory experience, and negatively impact the application’s overall rating. Android application crashes can be avoided through intensive and extensive testing. In the related literature, the graphical user interface (GUI) test generation tools focus on generating tests and exploring application functions using different approaches. Such tools must choose not only which user interface element to interact with, but also which type of action to be performed, in order to increase the percentage of code coverage and to detect faults with a limited time budget. However, a common limitation in the tools is the low code coverage because of their inability to find the right combination of actions that can drive the application into new and important states. A Q-Learning-based test coverage approach developed in DroidbotX was proposed to generate GUI test cases for Android applications to maximize instruction coverage, method coverage, and activity coverage. The overall performance of the proposed solution was compared to five state-of-the-art test generation tools on 30 Android applications. The DroidbotX test coverage approach achieved 51.5% accuracy for instruction coverage, 57% for method coverage, and 86.5% for activity coverage. It triggered 18 crashes within the time limit and shortest event sequence length compared to the other tools. The results demonstrated that the adaptation of Q-Learning with upper confidence bound (UCB) exploration outperforms other existing state-of-the-art solutions.

The exponential growth in the number of smart devices connected to the Internet of Things (IoT), and associated with various IoT-based smart applications and services, raises interoperability challenges which could affect the sustainability of IoT services. IoT software applications are built using different software platforms and embedded in diverse types of terminals and sensing devices. Aiming to offer smart services over a range of network technologies that use different communication protocols. The concept of Web service with service-oriented solutions was introduced to cope with the heterogeneity of hardware and software, and to tackle issues of interoperability, flexibility and scalability. The main step of this solution was the integration of Web of Things technologies into smart device networks, with the utilization of IoT gateways. Service management is a crucial factor in sustaining service-oriented solutions in dynamic and highly scalable IoT systems, and is concerned with several issues associated with service provisioning, orchestration, composition and adaption. This work was motivated by the need for robust and flexible service management systems that can meet the requirements for the rapid scalability and heterogeneity associated with the exponential growth of IoT systems. In the literature there is no survey of service management issues and associated research efforts in the field of IoT. In this article, we identify the key requirements for managing IoT services as well as common service management platforms for IoT. We provide a thematic taxonomy based on the important factors, and investigate recent advances in service management for IoT systems. Finally, the major challenges that remain open are presented as a guide for future research directions.

Deep Q-Network (DQN) is a popular deep reinforcement learning algorithm that has demonstrated promising results across a variety of domains. DQN presents a promising solution to the challenge of lowering false positives in software defect prediction, thereby enhancing the reliability of the prediction performance. In software defect prediction, false positives occur when the prediction model incorrectly predicts code changes to be defective. Consequently, developers waste time and resources on non-existent defects. This paper advocates for employing DQN in software defect prediction, focusing on minimizing false positives and maximizing the prediction performance. Throughout the training phase, the model learns to predict defect-prone following a reward policy aimed at reducing false results. Experimental findings show that the proposed DQN outperforms baseline classifier, improving the prediction accuracy of true defects by up to 27% when using only 20% efforts. The results show that the effectiveness of DQN in tackling false positives, thereby emphasizing the significance of incorporating dynamic reward in predicting software defects.

Social media slang, encompassing informal language, words, phrases, and acronyms on digital platforms, reflects the dynamic nature of online communication. Analyzing social media slang offers valuable insights for organizations and researchers, enabling a deeper understanding of communication trends, sentiment analysis, and user behavior in the digital sphere. It plays a pivotal role in shaping effective marketing strategies and enhancing communication, ultimately facilitating informed decision-making in the digital age. In our study, we conducted a systematic review of research articles from the Web of Science and Scopus databases, spanning the years 2016 to 2023. Our rigorous selection process, based on quality assessments as per PRISMA guidelines, revealed several following key findings. Social media slang exhibits a remarkable adaptability to different platforms, mirroring the communication styles and user cultures found on each. Notably, it influences user behavior, impacting interactions, content engagement, and decision-making, particularly in marketing and communication strategies. Furthermore, our research highlights the value of social media slang in sentiment analysis, providing insights into public sentiment and supporting well-informed decision-making. Our study underscores the versatile applications of slang analytics across various industries and research domains, emphasizing its pivotal role in providing specialized insights and enhancing communication strategies. In conclusion, our research offers a comprehensive understanding of the dynamic landscape of informal language in the context of contemporary digital communication, furnishing valuable insights that inform decision-making, refine marketing strategies, and enhance communication.