Multimedia applications have become an essential part of our daily lives, and their use is flourishing day by day. The area of wireless sensor network is not an exception where the multimedia sensors are attracting the attention of the researchers increasingly, and it has shifted the focus from traditional scalar sensors to sensors equipped with multimedia devices. The multimedia sensors have the ability to capture video, image, audio, and scalar sensor data and deliver the multimedia content through sensors network. Due to the resource constraints nature of WSN introducing multimedia will add more challenges, so the protocols designed for multimedia wireless sensor network should be aware of the resource constraints nature of WSN and multimedia transmission requirement. This paper discusses the design challenges of routing protocols proposed for WMSN. A survey and comprehensive discussion are given for proposed protocols of WMSN followed by their limitations and features.

Meeting the energy needs of a growing population is of paramount importance in today’s society. The use of renewable energy sources, especially solar energy, can help reduce greenhouse gas emissions from traditional sources. Solar irradiance, which depends on several factors such as the availability of sunlight, cloud cover index, latitude, orientation and tilt of solar panels, and technical factors, plays a crucial role in the use of solar energy. Predicting solar irradiance is therefore essential for enhancing the reliability and efficiency of solar energy systems. Artificial intelligence (AI) and machine learning (ML) models are increasingly being used to predict solar irradiance forecasting. These models can learn from historical weather data and identify complex patterns to predict future solar irradiance with high accuracy. Techniques such as regression, neural networks and ensemble methods are frequently used and enable more efficient planning and operation of solar energy systems. This study provides a comprehensive analysis of the existing state-of-the-art models for solar irradiance forecasting. The study evaluates a variety of forecasting models, including machine learning, numerical weather prediction and hybrid approaches for assessing their accuracy, strengths and weaknesses. As an outcome of this study, potential future improvements in the prediction of solar irradiance are highlighted and the importance of interdisciplinary collaboration and emerging technologies is emphasized. To ensure a sustainable and resilient energy future, it is crucial to continue efforts to better integrate solar energy into mainstream electricity systems. This work can serve as a fundamental analysis for future researchers to identify the most appropriate approaches for medium and long-term solar irradiance forecasts.

The Internet of Things (IoT) and wireless sensor networks (WSNs) have evolved rapidly due to technological breakthroughs. WSNs generate high traffic due to the growing number of sensor nodes. Congestion is one of several problems caused by the huge amount of data in WSNs. When wireless network resources are limited and IoT devices require more and more resources, congestion occurs in extremely dense WSN-based IoT networks. Reduced throughput, reduced network capacity, and reduced energy efficiency within WSNs are all effects of congestion. These consequences eventually lead to network outages due to underutilized network resources, increased network operating costs, and significantly degraded quality of service (QoS). Therefore, it is critical to deal with congestion in WSN-based IoT networks. Researchers have developed a number of approaches to address this problem, with new solutions based on artificial intelligence (AI) standing out. This research examines how new AI-based algorithms contribute to congestion mitigation in WSN-based IoT networks and the various congestion mitigation strategies that have helped reduce congestion. This study also highlights the limitations of AI-based solutions, including where and why they are used in WSNs, and a comparative study of the current literature that makes this study novel. The study concludes with a discussion of its significance and potential future study topics. The topic of congestion reduction in ultra-dense WSN-based IoT networks, as well as the current state of the art and emerging future solutions, demonstrates their significant expertise in reducing WSN congestion. These solutions contribute to network optimization, throughput enhancement, quality of service improvement, network capacity expansion, and overall WSN efficiency improvement.

Recent advances in information and communication technology have greatly increased the popularity of online education. The COVID-19 pandemic has further accelerated the adoption of online education due to the need for educational institutions to adopt online learning models. On the other hand, the online education system faces several challenges, such as lack of usability to overcome access barriers and scalability issues. These problems stand in the way of widespread adoption of the online education system. The usability of online education websites is one of the fundamental challenges because it directly affects students’ learning experience. This study focuses on the challenges related to usability of online education websites. If an online education website has usability issues, students may have difficulty using the learning materials provided. This makes it difficult to achieve the required level of service, which can lead to a poor reputation for the website. Therefore, in order to develop a well-functioning online education website, it is necessary to ensure that all usability requirements (URs) are met efficiently. Considering the different backgrounds, knowledge, and skills of students, they may have different preferences regarding usability requirements. Therefore, prioritization of URs is necessary to focus on the most preferred requirements. In this study, we apply the fuzzy analytic hierarchy process (FAHP) to analyze the usability requirements in the requirements analysis phase. We consider the online education website of Virtual University of Pakistan, a leading online university in the country with more than 0.1 million active students. The result of this work is a flexible approach to identify precise usability requirements for developing a functionally sound website with an optimized and easy-to-use user interface that will ultimately improve the quality of education.