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This work presents an image compression technique designed for wireless sensor networks (WSNs), leveraging autoencoders and incorporating an error-bound mechanism. The algorithm strategically reduces redundancies in image data, conserving energy by exploiting spatial and temporal correlations within sampled image data through autoencoder features. Precise control over the distortion level in reconstructed images is achieved via the error-bound mechanism, establishing equilibrium between compression rate and reconstruction error. Evaluation results demonstrate comparable image reconstruction fidelity to existing methods (JPEG, JPEG2000, HDPhoto, and an existing Rate-Distortion Balanced approach). The proposed algorithm achieves superior image reconstruction quality at compression ratio rates exceeding 70%, emphasizing a fundamental approach prioritizing heightened reconstructed image quality while balancing compression ratio, distortion, and energy efficiency. Notably, a substantial 50% reduction in overall energy consumption is realized at a compression rate of 38.6%.

Research in Remote Patient Monitoring Systems (RPMS) is considered to be one of the most crucial fields since it deals with human lives. The rise in usage of RPMS has increased since the emergence of the pandemic. Even though there is a rise in these systems, there are some challenges, such as mobility, heterogeneous networks, standardization of RPMSs, automation, and Quality of Service (QoS). Our discussion focuses on RPMS systems for physiological parameter monitoring in the areas of their applications, architecture, and challenges. Thus, an in-depth review of RPMS and the analysis of these data are performed in order to understand where the current RPMS literature stands. The literature shows that research in these RPMS is concentrated on two or more of the following areas: applications, architecture, methodologies, and their performance. It appears that prior to 2020, researchers focused on nearly all aspects of RPMS until the pandemic. Then there was a shift in RPMS research to focus more on the applications and architectures of these systems. As a result, more companies are developing mobile RPMS. In this paper, we present a detailed of various existing RPMS with areas of focus on their application, architecture, technology applied, and challenges faced. We further provided a comparative and statistical analysis of the existing literature, and, finally, an overview of Quality of Service (QoS) as one challenge of RPMS is provided. The surveyed QoS requirements based on traffic type, data quality, device quality and network metrics are provided, with the aim of providing the current trend for researchers and industries to adapt to the best approach in the design of quality-aware RMPS. We then conclude the work by providing future work, which, when adopted, will brighten the future of RPMS deployment.

Intensive research has been done on image mosaic algorithms to improve the field of view through generated image mosaics. However, their usage varies from one field to another due to the challenges faced by image acquisition platforms. Moreover, the current imagery software packages used are computationally intensive to be used in real-time applications and are not economically affordable. Those that are open-source are limited due to the less amount of data used to test their mosaicing algorithms’ performance. Therefore, detailed knowledge of appropriate mosaic algorithms suitable for real-time applications is needed to produce mosaics with less computational time and efficient feature point detection. A comprehensive survey that categorizes existing mosaic algorithms’ adoption in various fields has not been done to the best of our knowledge. Firstly, we provide a comparison of the strengths, weaknesses, and uniqueness of the image mosaic algorithms across different fields, with emphasis on challenging issues, limitations, performance criteria, and mechanisms. Furthermore, this paper provides an up-to-date review of image mosaic algorithms in various domains as used in different fields. We further classify the usage of image mosaic algorithms based on the following domains: spatial, frequency, and combined spatial and frequency as used in agriculture, environmental monitoring, and medical imaging. In addition, an analysis was carried out on one of the promising algorithms based on improved SIFT with the aim of improvement. We then proposed an improved SIFT algorithm, which was then evaluated with an open-source algorithm and commercial software using structural similarity index measure (SSIM) and mosaicing computational times for mosaic accuracy and processing efficiency, respectively. Our approach demonstrated a significant improvement of more than 10% average on the mosaicing computational times for the five datasets used. Its mosaicing accuracy was found to be relatively within an acceptable range of above 90% averagely.